HK1106583A - Method and apparatus for measurement processing of satellite positioning system (sps) signals - Google Patents
Method and apparatus for measurement processing of satellite positioning system (sps) signals Download PDFInfo
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Description
This application is a divisional application of application No. 00805127.5 filed 24.1.2000 entitled "method and apparatus for Satellite Positioning System (SPS) signal measurement processing".
Reference to related invention
This application is a continuation-in-part application serial No. 09/109,112 filed on month 2, 1998.
Technical Field
The present invention relates generally to the field of satellite positioning systems, such as Global Positioning System (GPS) receivers, and more particularly to processing SPS signals.
Background
Global Positioning System (GPS) receivers typically determine their position by calculating the time of arrival of signals transmitted simultaneously from multiple GPS (or NAVSTAR) satellites. These satellites transmit as part of their information satellite positioning data and clock timing data, all known as "ephemeris" data. The process of searching for and acquiring GPS signals, reading ephemeris data for a plurality of satellites, and calculating the receiver position from these data is time consuming, typically requiring several minutes. In many cases, such long processing times are unacceptable, and in addition, it greatly limits battery life in miniaturized portable applications.
The GPS receiving system has two main functions. The first is to compute the pseudoranges to the various satellites, and the second is to compute the position of the receiver using these pseudoranges and the satellite's timing and ephemeris data. The pseudorange is simply the time of arrival of the satellite signal measured by the local clock. The definition of pseudoranges is sometimes also referred to as code phases. Once the GPS signals are acquired or tracked, satellite ephemeris and timing data is extracted therefrom. As mentioned above, collecting this information usually takes a considerable amount of time (30 seconds to several minutes) and must be done with a good received signal level in order to get a low error rate.
Most GPS receivers compute pseudoranges using correlation methods. These correlation methods are typically performed in real time with a hardware correlator. The GPS signal includes a signal of high repetition rate called a pseudo-random sequence. The civilian code is called the C/a (coarse/acquisition) code and has a binary inversion rate or "chip" rate of 1.023MHz and a repetition period of 1023 chips for each millisecond of the code period. The code sequence belongs to the Gold code family, and each GPS satellite transmits a signal along with a unique Gold code.
For a signal received from a given GPS satellite, after a down conversion process to baseband, the associated receiver multiplies the received signal by a stored copy of the appropriate gold code contained in its local memory, and then integrates or low pass filters the result to obtain an indication of the presence of the signal. This process is called a "correlation" operation. By adjusting the relative timing of stored replicas in time with respect to the received signal and observing the correlation output, the receiver can determine the delay time of the received signal and the local clock. The initial determination that this output exists is called "capture". Once acquisition has occurred, the process enters a "tracking" phase, in which small adjustments are made to the timing of the local reference in order to maintain a high correlation output. The correlation output of the tracking phase can be thought of as a GPS signal with the pseudorandom code removed, or "despread" in general terms. The signal is narrowband, with a bandwidth equivalent to a 50bit per second Binary Phase Shift Keying (BPSK) data signal, which is superimposed on the GPS waveform.
The correlation acquisition process is time consuming, especially when the received signal is weak. To improve acquisition time, most GPS receivers utilize multiple correlators (typically close to 36), which allows parallel searching for correlation peaks.
The associated GPS receiving device is typically designed to receive GPS signals in open space because the satellite signals are directly visible and therefore blocked by metal and other materials. The improved signal sensitivity provided by the GPS receiver allows for indoor tracking of GPS satellite signals, or tracking in the presence of weak multipath signals and total reflection signals. The ability to capture these weak signals causes other problems. For example, tracking strong and weak signals simultaneously may cause the receiver to lock onto a cross-correlation signal that is not a true signal. It is possible to capture a stronger cross-correlation peak rather than finding a weaker true peak. Tracking a weaker satellite signal does not guarantee that it is a direct signal. The weak signal may be a reflected signal or a combination of a direct signal and an indirect signal. The combined signal is considered to be a multipath signal. The path of the reflected signal is typically longer than the path of the direct signal. The difference in path length causes the measurement of the arrival time of the reflected signal to be delayed or the measurement of the corresponding code phase to contain a positive offset. The magnitude of the offset is generally proportional to the associated delay between the reflected and direct paths. The possible absence of a direct signal component nullifies existing multipath mitigation techniques (e.g., narrow correlators or gated correlators).
It is therefore desirable to provide a measurement processing algorithm that optimally utilizes various existing data to obtain optimal position results.
Disclosure of Invention
Methods and apparatus for SPS signal measurement processing are disclosed. In one embodiment of the invention, a GPS receiver receives a plurality of GPS signals transmitted from a corresponding plurality of GPS satellites. The signal environment of the location at which the GPS receiver is located is characterized to produce signal environment data. In an exemplary embodiment, an information source, such as a cellular network based database, is searched to retrieve signal environment data that gives the approximate location of the GPS receiver. The approximate location may be determined by a cell site location in cellular wireless communications with a cellular communication device co-located with the GPS receiver. One or more parameters relating to the characteristics of the satellite signal are defined. Threshold values for the parameters are determined using the signal environment data. Code phases of times of arrival of respective satellite signals transmitted by a plurality of satellites are measured. Data representing the measured time of arrival is examined using threshold values of the parameters to produce a set of times of arrival from which a position result of the GPS receiver is calculated.
In another embodiment of the present invention, the signal environment at the location of the GPS receiver is characterized to produce signal environment data. The signal environment data reflects the manner in which SPS signals are propagated at the location. The signal environment data is used to determine at least one processing value that is used to, in turn, process data indicative of SPS signals received by the GPS receiver.
In a particular embodiment of the invention, a cell based information source (e.g., a database based on a cellular telephone network) is used to determine signal environment data indicative of the manner in which SPS signals are propagated at a location at which an SPS receiver is located, and the SPS receiver at that location processes the SPS signals in a manner determined by the signal environment data.
In another embodiment of the present invention, a method of processing SPS signals is capable of determining the presence of two (or more) correlation peaks for a same set of SPS signals from a first SPS satellite. A set of measurements representing the time of arrival of the SPS signal is from two (or more) correlation peaks; typically, the earlier correlation peak represents a direct path of a set of SPS signals, rather than a reflected path, and the earlier correlation peak is used to derive a measurement representing the time of arrival of a set of SPS signals.
Other features and embodiments of the invention will be apparent from the accompanying drawings and from the detailed description that follows.
Drawings
The present invention is illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate corresponding parts, and in which:
FIG. 1 is a block diagram of one example of a GPS receiving system utilizing an embodiment of the present invention, showing a data link that may exist between a base station and a remote GPS receiver.
FIG. 2 is a table illustrating an example of a data element with example values for seven different viewpoint satellites that may be used with embodiments of the present invention.
Fig. 3 is a graphical representation of fifteen correlator outputs with 1/2 chip delay amplitudes, which are near the dotted channel of the satellite as shown in fig. 2.
Fig. 4 is a flowchart outlining the primary operation of the GPS receiver 20, or other type of GPS receiver, or mobile GPS receiver in combination with a data processing system such as a server computer system, in processing received satellite signals to generate position coordinates in accordance with a measurement processing method of an embodiment of the present invention.
Fig. 5 is a flowchart outlining an operation included in the signal environment characterization process shown in fig. 4 according to an embodiment of the present invention.
FIG. 6 is a flowchart outlining an operation included in the algorithmic control parameter setup process shown in FIG. 4 in accordance with an embodiment of the present invention.
Fig. 7 is a flowchart outlining an operation included in the measurement value selection and calculation process shown in fig. 4 according to an embodiment of the present invention.
Fig. 8 is a flowchart outlining an operation included in the error detection and separation process shown in fig. 4 in accordance with an embodiment of the present invention.
Fig. 9 is a flowchart outlining an operation included in the offset adjustment process shown in fig. 4 according to an embodiment of the present invention.
FIG. 10 is a flowchart outlining an operation included in the sequential measurement optimization process shown in FIG. 4 in accordance with an embodiment of the present invention.
Fig. 11 is a flowchart outlining an operation included in the calculation and error estimation process shown in fig. 4 in accordance with an embodiment of the present invention.
Fig. 12A is an example of a cellular network system including a cell-based information source.
Fig. 12B is an example of an SPS server according to an embodiment of the present invention.
Detailed Description
Methods and apparatus for Satellite Positioning System (SPS) signal measurement processing are described.
In the following discussion, embodiments of the present invention will be described with reference to the application of the United states Global Positioning System (GPS) system as an example of an SPS system. It is apparent, however, that these methods are equally applicable to other satellite positioning systems, such as the russian Glonass system. Thus, the term "GPS" as used herein includes such alternative satellite positioning systems, including the Russian Glonass system. Likewise, the term "GPS signals" includes signals transmitted by alternative satellite positioning systems.
Furthermore, although embodiments of the present invention are described with reference to GPS satellites, it is appreciated that the teachings are equally applicable to positioning systems using pseudolites or a combination of satellites and pseudolites. Pseudolites are ground-based transmitters that transmit a PN code (similar to a GPS signal) modulated on an L-band (or other frequency) carrier, typically synchronized with GPS time. Each transmitter is assigned a unique PN code so that it can be recognized by a remote receiver. Pseudolites are useful in places where GPS signals from an orbiting satellite might be difficult to reach, such as tunnels, mines, buildings, urban canyons, or other enclosed areas. The term "satellite" as used herein is intended to include pseudolites or equivalents of pseudolites, and the term GPS signals as used herein is intended to include pseudolites or equivalents of pseudolites that originate GPS signals from pseudolites or equivalents of pseudolites.
GPS receiving system
Fig. 1 is a block diagram of a GPS receiving system that can implement the method of the present invention. The GPS receiving system of fig. 1 includes a mobile or remote GPS receiver unit 20 which includes a GPS processing stage and a communications stage. Thus, the GPS receiver unit 20 includes circuitry for performing the functions required for processing GPS signals and for processing communication signals transmitted and received over the communication link. A typical communication link, such as a data link 16, is a radio frequency communication link (e.g., a cellular telephone communication link) to another communication component, such as a base station 10 having a communication antenna 14.
In accordance with conventional GPS methods, the GPS receiver 20 receives GPS signals transmitted by orbiting GPS satellites and determines the time of arrival of a unique pseudo-random noise (PN) code by comparing the time variation between the received PN code signal sequence and an internally generated PN signal sequence. The GPS signals are received by a GPS antenna 40 and input to acquisition circuitry that acquires the various received satellite PN codes. Navigation data (e.g., pseudorange data) generated by the acquisition circuit is processed by the processor and transmitted to the data communication link 16.
The GPS receiver 20 also includes a communications transceiver section, shown as a modem 22, for communicating with the data link 16. The modem 22 is coupled to a communications antenna 24. The modem 22 transmits the navigation data processed by the GPS receiver 20 to a remote base station, such as the base station 10, via a communication signal (typically a radio frequency signal). The navigation data may be the actual latitude, longitude and altitude of the GPS receiver, or may be raw or partially processed data. The received communication signal is input to the modem 22 and passed to the processor for processing, and possibly output through an audio speaker.
According to one embodiment of the invention, pseudorange data generated by the GPS receiver 20 is communicated to the base station 10 via the data link 16. The base station 10 then determines the position of the GPS receiver 20 based on a combination of pseudorange data from the receiver, the time at which the pseudorange was measured, ephemeris data received by the GPS receiver itself (e.g., via the GPS antenna 12), or other similar data source, such as a GPS reference receiver network. The location data may then be transmitted back to the GPS receiver 20 or other remote location. The data link 16 between the GPS receiver 20 and the base station 10 may be implemented in a variety of different embodiments, including a direct link, or a cellular telephone link, or any other type of wireless link.
In one embodiment of the invention, the base station 10 commands the GPS receiver 20 to make a position measurement via a message sent over the data link 16. In this message, the base station 10 also transmits doppler related information (or other information, such as a satellite almanac from which doppler frequencies can be derived) to the satellites in view. The doppler related information may also include a mathematical representation of doppler rate of change, doppler acceleration or other doppler related information. The doppler information is in the form of satellite data information relating to satellite position and velocity, typically in the form of frequency information. Typically, the message also specifies the identity of a particular satellite within visual range, or other initialization data. The modem 22 receives the message and stores it in a memory 30 coupled to the microprocessor 26. Microprocessor 26 handles the direct transfer of data information between remote processing elements 32-48 and modem 22.
Typically, the doppler information included in the message is very short in duration because the accuracy required for the doppler information is not high. For example, if the required accuracy is 10Hz and the maximum Doppler frequency is about 7kHz, then an 11-bit word will suffice for each satellite in view. The remaining bits will be used to convey doppler rate of change information. If there are eight satellites in view, then 88 bits are required to specify all doppler frequencies. The use of this information eliminates the need for the remote device 20 to search for these doppler frequencies, thus reducing the processing time by more than a factor of ten. The use of doppler information also allows the GPS receiver 20 to more quickly process samples of GPS signals, such as collecting digitized GPS signals stored in digital memory.
When the GPS receiver 20 receives a command (e.g., from the base station 10) to process GPS signals via a message containing doppler information, the microprocessor 26 activates the RF-IF converter 42, analog-to-digital (a/D) converter 44 and digital fast-slow memory 46 via the battery and power regulator and power switching circuit 36 (and controlled power lines 21a, 21b, 21c and 21D) to provide full power to these components. This causes the signals from the GPS satellites received by the antenna 40 to be down-converted from Radio Frequency (RF) to an Intermediate Frequency (IF) and then digitized in the a/D converter 44. A continuous set of data is then stored in the snapshot storage 46, typically for a data duration of one hundred milliseconds to one second (or longer). The addressing of the memory 46 to store this data is controlled by a Field Programmable Gate Array (FPGA) integrated circuit 48. The down conversion of the GPS signal is accomplished by using a frequency synthesizer 38, which frequency synthesizer 38 provides a local oscillator signal 39 to a converter 42, which will be discussed in detail below.
The DPS microprocessor 32 may remain in a low power state during which the snapshot memory 46 is filled with digitized GPS signals from satellites within view. RF-IF converter 42 and a/D converter 44 are typically only on for a short period of time sufficient to collect and store the data needed for pseudorange calculation. After the data collection is complete, these switching circuits are turned off or otherwise powered down by controlling the power lines 21b and 21c (at which time the memory 46 continues to receive full power), so that no additional power is wasted during actual pseudorange calculations. Pseudorange calculations are then performed, using, in one embodiment of the invention, a general purpose programmable Digital Signal Processor (DSP) integrated circuit IC 32. Prior to making this calculation, the microprocessor 26 and circuitry 36 place the DSP 32 in an active power state by controlling the power line 21 e.
In one embodiment, the DSP 32 is a general purpose programmable processor, as opposed to a custom digital signal processor, which is used for other GPS units. Once the DSP 32 has completed the calculation of the satellite pseudoranges for each of the ranges of visibility, it calculates the final position of the satellite using satellite ephemeris data provided over a data link or collected by standard demodulation techniques. Alternatively, it may transmit the pseudoranges to a base station (e.g., base station 10) that provides a final position calculation. Fig. 12A shows an example of a base station, which in this case may be referred to as an SPS server. The SPS server is communicatively coupled to the SPS receiver/cellular telephone via a cellular telephone network and a Public Switched Telephone Network (PSTN).
In one embodiment of the invention, the DSP 32 sends the position information to the microprocessor 26 over an interconnect bus 33. The microprocessor 26 may then cause the DSP 32 and memory 46 to again enter the low power state by sending an appropriate control signal to the battery and power conditioning circuit 36. The microprocessor 26 then transmits the pseudorange data or position data to the basestation 10 via the data link 16 using the modem 22, and performs a final position calculation or output on a display device (not shown).
Depending on the amount of data stored in the digital snapshot storage 46 and the speed of the DSP, it is expected that the calculation of the position in the DSP will typically take less than a few seconds.
As described above, the digital snapshot storage 46 captures a record of a relatively long time. Efficiently processing the large block of data using fast convolution enhances the efficiency of the present invention in processing low receive level signals. An example of this method is described in U.S. patent No. 5,663,743. The pseudoranges for all visible GPS satellites may be computed using this same buffered data. This improves the performance of a continuous tracking GPS receiver in situations where the signal amplitude changes rapidly (e.g. in urban blockage situations).
Different GPS receiver architectures may be used with the present invention. Although the above discussion has focused on a GPS receiver with digital memory and a digital signal processor, other GPS receiver configurations may implement all or part of the method of the present invention and may be implemented as all or part of the apparatus of the present invention. For example, a conventional hardware correlator-type GPS receiver may be used with the present invention. GPS receivers of the type described in PCT application PCT/US98/07471 or PCT/US98/11375 or PCT/US97/06946 (publication No. 97/40398) may also be used with the present invention. In each case, the correlator output (see FIG. 3) for each SPS satellite is stored for use in the measurement processing techniques of the present invention.
In each case, the receiver itself may perform the complete processing of one of the measurement processing techniques; or perform a portion of the processing of the technique and forward the intermediate processing results to a "server" system, such as the SPS server shown in fig. 12A, where the processing is accomplished by the server system. In the case where a GPS receiver (e.g., mobile unit 20) performs all of the processing for one of these techniques, the GPS receiver may receive assistance data from an external source over its communication link, such as a cellular telephone system; the assistance data, such as characteristics of the signal environment (and associated parameter values), may be transmitted to mobile unit 20. Where the GPS receiver performs some processing, the GPS receiver typically stores samples of the correlator output of each SPS satellite (such as the data shown in fig. 3) and then transmits the correlator outputs to a server, which performs the measurement processing techniques of the present invention.
Measurement value processing
In one embodiment of the invention, the improved GPS receiver processes several different types of data for each visible satellite. These data types may include: one code phase corresponding to the maximum correlation peak (also called the main peak) (measured time of arrival); a set of code phases around the maximum correlation peak (e.g., computed at 1/8, 1/4, 1/2C/a code chips or other arbitrary spacing); a set of correlation peak widths (calculated at some selected signal level, e.g., 3dB below peak); doppler frequency (measuring code phase rate of change); signal-to-noise ratio (SNR); signal-to-interference ratio (SIR); an integration period; a marker indicating the presence of multiple peaks and their relative positions to the main peak.
In an exemplary embodiment of the invention, the signal environment is characterized by these types of data measured for SPS signals (e.g., SNR of SPS signals), which represents the manner in which SPS signals propagate locally (e.g., not in the ionosphere) at the location of the SPS receiver. In a typical example, the locally propagated SPS signals are SPS signals that are propagated within a range of approximately 1000 meters near the location of the SPS receiver. That is, SPS signals that propagate within a range of approximately 1000 meters near the SPS receiver location may be considered locally propagating SPS signals; this distance may be considered to range from about 0.1 meters from the SPS receiver to about 1000 meters (perhaps 2000 meters) from the SPS receiver. In another embodiment, the signal environment is characterized by the type of data measured for the cellular communication signal (e.g., the SNR of the received cellular telephone communication signal). For example, one or more of these data types may be determined for a received cellular communication signal, or may be derived from the power level of the transmitter in the cellular telephone (e.g., a higher power level may indicate a lower SNR). The signal environment characterized by measurements of cellular communication signals generally represents the manner in which SPS signals are propagated locally at the location of the SPS receiver. It is also noted that processing of cellular communication signals may utilize signal environment characterization.
FIG. 2 is a table illustrating some instances of data elements with example values for seven different viewpoint satellites that may be used in one embodiment of the invention. The satellites are numbered 2, 4, 7, 9, 15, 26 and 27 according to their corresponding PRN codes. The data for each satellite includes signal-to-noise ratio, signal-to-interference ratio, peak width, azimuth, elevation, code phase, and doppler information.
Fig. 3 shows an example of a folded amplitude of fifteen correlator outputs with 1/2 chip delays near a dotted channel. The waveform of figure 3 corresponds to the data values for satellite number 27 shown in the table of figure 2. Fig. 3 illustrates an example of a "double peak" characteristic, demonstrating the presence of two strong signals. Fig. 3 also demonstrates that there are two weak signals, one at 3 half-chip (half-chip) correlation delay times and the other at 11 half-chip correlation delay times. These signals can all be measured by the time of arrival of the direct signal. A typical case of dual-peak characteristics of the same SPS satellite signal occurs when an SPS receiver receives both the reflected signal and the direct signal from the same SPS satellite signal, both of which are relatively strong and exceed the signal detection level. Typically, the direct signal produces an early correlation peak (e.g., time ≈ about 6 and y ≈ about 4500 as shown in FIG. 3), and the reflected signal produces a late correlation peak (e.g., time ≈ about 8.5 and y ≈ about 6500 as shown in FIG. 3). In certain situations (e.g., SPS satellites are below the horizon), the reflected signal may be stronger than the direct signal; fig. 3 shows this example.
Another type of interference, known as cross-correlation interference, may also exist and occur when a stronger SPS satellite signal interferes with the processing of a weaker signal; an example of a method of mitigating this interference is described in co-pending U.S. patent application serial No. 09/109,112, filed on 2.7.1998.
Fig. 4 is a flowchart outlining the primary operation of the GPS receiver 20 in processing received satellite signals to generate position information according to a measurement processing method of an embodiment of the present invention. The measurement value processing method shown in fig. 4 includes seven main processes. In operation 101, a signal environment of a GPS receiver location is characterized. Experimental data represents the significant variation of signal characteristics, such as various measurements of signal strength, pseudorange and multipath error, from one environment (e.g., urban canyons) to another (e.g., indoors). This is mainly because the distribution of building materials, weight and space in different urban environments affects the access of the satellite signals to the GPS receiver antenna. The signal environment data indicates the manner in which SPS signals are propagated to SPS antennas at a particular location.
The cellular sites that characterize the communication of the GPS receiver 20 according to its signal environment facilitate the algorithm selection for measurement processing. This information may be obtained as part of the cell target information. In addition to cell signal classification, cell target information may also include cell service area, cell site identification, cell site location, and estimated altitude information. Various signal environments may be defined as "open sky", "country", "city", "urban canyon", and so on. An "urban canyon" can be subdivided by real cities or major cities to more precisely define an environment, such as a "tokyo urban canyon" or a "san francisco urban canyon". For example, "minneapolis urban canyons" represent flat terrain, whereas "san francisco urban canyons" represent hilly terrain that may have significant variations in elevation.
In an exemplary embodiment of the present invention, the characterization of the signal environment is performed each time the SPS receiver is operating at a location without reference to a prior analysis of the signal environment at that location. However, it is possible to use a previous analysis of the location signal environment and to consider this previous analysis as a set-up step. The signal environment of a location, such as the location of a cell site or the typical location of a cellular telephone within the cell site's serving cell area, may be characterized in the establishing step (e.g., prior analysis) by several measurements recorded at that location (or several "representative" locations within the cell site's coverage area). Because the satellites move around, the experimental data is valid only for a certain period of time. The above measurements may be repeated at different times of day or around the peak usage time. These measurements are actually analyzed to determine typical signal strength and typical peak width (e.g., SNR output and/or SIR output and/or peak width as shown in fig. 2), interference patterns and the presence of multipath errors. Because the location (or representative location) is known when the build-up characterization is made, the satellite signal correlation output can be compared to an expected correlation output, which will show the number of reflections (e.g., reflected signals) and the presence of double peaks in the signal environment. In another example, a correlation output that differs from the RMS (root mean square) of the nominal value can be used to analyze multipath error. Also, the actual knowledge that most or all of the cell sites cover a city or a countryside may be included in the signal environment data. Once characterization is complete, the data characterizing the signal environment is stored in a database (e.g., a cell-based database) where locations in the database (e.g., cell site identifiers or other cell location identifiers) are associated with the data characterizing the signal environment. Fig. 12A shows an example of this database maintained in the SPS server. In operation of one embodiment of the present invention, a mobile cellular telephone/GPS receiver (e.g., receiver 901b) provides pseudoranges and associated outputs (for measurement processing) that are transmitted to a cell site (e.g., cell site 901 a). The cell site then forwards the data to an SPS server (e.g., server 912) which in turn determines the signal environment (e.g., receives the identifier of the cellular radio communication cell site and looks in a database (e.g., database 912a) for signal environment data associated with the cell site location). The pseudoranges and correlation outputs received from the mobile cellular telephone/GPS receiver (e.g., receiver 901b) are then processed with data representing the signal environment, according to various embodiments of the invention. This signal environment data can be supplemented by dynamically derived signal environment measurements that are obtained in the actual application of the system after the characterization is established. However, establishing a characterization may provide help information; for example, in a particular cell of a cellular telephone network, most of which are in a city or a country, this information will be passed to the mobile SPS receiver and used by the mobile SPS receiver as part of the signal environment classification.
In one embodiment of the present invention, the environment classification obtained in operation 101 may be used to assist in algorithm control parameter selection in operation 103. The control parameter establishment in operation 103 typically includes signal-to-noise ratio, signal-to-interference ratio, peak width, HDOP, a characterization code for satellite elevation, and other parameters. These control parameters are used in the measurement selection and calculation process of operation 105. And selecting the measured value according to the parameter characterization code value. As part of operation 105, some measurement calculations are also performed. For example, the input signal-to-noise ratio is estimated from the measured (output) signal-to-noise ratio, the measurement value integration period (defined in terms of the integration number before detection and the integration number after detection), and the doppler error.
Based on the parameter selection of operation 103, some measurements may be identified as potential cross-correlations. In operation 105, a test is performed to determine if the receiver that produced the code phase measurement is a cross correlation peak, rather than a true signal.
The error detection and separation (FDI) step of operation 107 then uses these measurements for successful pass through operation 105. The error detection and separation step serves to separate (i.e., identify) any erroneous satellites so that they can be corrected or removed from the results. A prerequisite for error detection and separation is an overdetermined solution, i.e. the number of measured values exceeds the unknown number.
If there is an error (offset) in the pseudorange measurements, an offset adjustment step is performed at operation 109. In one embodiment, the offset adjustment step first performs offset estimation and then performs offset adjustment. This step may also include correcting the pseudorange measurements with the offset estimate.
In operation 111, Sequential Measurement Optimization (SMO) steps are used to improve selected error statistics. The error statistics used may be affected by the characterization of the signal environment in operation 101. The sequential measurement optimization step analyzes the solutions one subset at a time for each measurement and selects the solution with the best error statistic. For example, if there are n measurements, with only one error, the sequential measurement optimization step will consider the solutions of the n subsets to be obtained by ignoring one satellite at a time in the original set. In an alternative embodiment of the present invention, the satellite measurements are adjusted by the error estimates calculated in operation 109 without the method of removing the satellite. In this way, the sequential measurement optimization step analyzes all possible subsets to obtain the best solution. In another embodiment, the offset adjustment may be made as part of a sequential measurement optimization step.
The position and velocity are calculated in operation 113. In addition, error statistics such as unit variance, estimated horizontal error, weighted HDOP, error ellipses and their orientations are also computed.
A detailed description of the individual operations in each of the main processes of fig. 4 will be provided in the following sections.
Signal environment characterization
Fig. 5 is a flowchart outlining an operation included in the signal environment characterization process as shown in operation 101 in fig. 4 in accordance with an embodiment of the present invention. Identifying or determining the signal environment of a GPS receiver is important to achieve maximum adaptability and support various operating levels.
In operation 201, a signal environment is divided into "indoor" and "outdoor". In one embodiment of the invention, this information is provided to the GPS receiver by user input. In another embodiment, this information may be separated from the obtained GPS based measurements. For example, the signal-to-noise ratio and/or distribution of signal attenuation and satellite line-of-sight information (azimuth and elevation) may be indicative of an indoor environment or an outdoor environment. The signal attenuation is calculated as the difference between the measured input signal level and the desired input signal level. The desired signal level is the signal level of the unobstructed direct satellite signal for a given combination of elevation and azimuth. The desired input signal level is determined as a function of the doppler error and the total integration period. The antenna gain pattern of the GPS receiver may be used to adjust the desired signal level.
For example, if the satellite signal is attenuated by a threshold level for all visible ranges, the signal environment may be identified as "indoors". The presence of short multipath errors (< 30m) on all or most satellites can also identify an indoor environment. In another example, a signal environment may also be identified as "indoor" in the event that there is at least one satellite signal at a higher elevation angle that exhibits an attenuation level higher than the signal level emitted by a low-altitude satellite. There is a peak width shift across all or most satellites, and typically in the waveform where the peaks are wide, the indoor environment can also be identified. Under certain signal conditions, such as signal combinations of different phases, the peak width narrows as a result of the presence of multipath signals.
In another embodiment of the present invention, the signal level transmitted from the cellular output (e.g., from the base station to the handset) is used to assist in the determination of the signal environment. In a manner similar to that described above with respect to GPS satellite signals, signal attenuation measurements of cellular or radio signals may be used to help determine whether a combined GPS receiver, such as GPS receiver 20, is used indoors or outdoors.
In operation 202, it is determined whether the signal environment is "outdoors". If the signal environment is indoors, the process of operation 207 is performed, thereby skipping operation 203 and 205. Additionally, the process may also skip operation 203-209 because the indoor environment is unlikely to have dynamic characteristics (even if it does, indicating that the characterization of the indoor may be incorrect, the signal environment should be re-characterized as "outdoor"). If the environment is outdoors, the environment is subdivided into "open sky", "country", "city", or "urban canyon" in operation 203. In one embodiment of the present invention, these fine classifications are determined by further analyzing the signal attenuation and pseudorange error signatures of the GPS signals. For example, if a GPS sensor is capable of acquiring and tracking satellite signals in all visible ranges that exhibit little or no multipath direct signal characteristics, then the environment may be declared to be an "open-sky" signal environment.
For operation 203, the signal attenuation/signal blocking information is used to determine the type of "city" environment. For example, in an urban environment, assuming buildings spaced 20 meters apart, a 30 satellite visibility profile is equivalent to being surrounded by buildings that are 6 meters high. Satellite visibility mask information comes from total signal blockage or a particular level of signal attenuation. For example, with a direct signal environment receiving all over 60 ° elevation signatures, it may be declared an "urban canyon" signal environment. A typical urban signal environment is one in which the satellites are hidden by buildings but have better visibility in the vertical direction. Pseudorange measurements with large multipath errors may also be indicative of an urban canyon environment. In many cases, the presence of multiple peaks or offsets in the peak shape may also be indicative of an urban canyon environment.
In operation 204, it is determined whether the outdoor signal environment is an urban or urban canyon, or an open sky or a country. If the outdoor signal is classified as an "urban" or "urban canyon" environment, the environment is further subdivided by identifying its urban area or urban name in operation 205. For example, an urban canyon environment may be designated as a "chicago urban canyon" or a "san francisco urban canyon". As previously mentioned, the actual urban environment can greatly affect the reception of GPS signals through the urban or natural terrain and the type of buildings surrounding the GPS receiver.
In one embodiment of the invention, the information is derived from cell target information. In another embodiment, the information is provided by user input. Alternatively, it may be derived from the initial GPS position results. The initial coordinates, which are independent of the particular urban canyon information, are generally correct enough as a database for identifying urban canyon searches. In another embodiment, the initial position information may be derived in combination with other positioning methods, such as terrestrial radio positioning using wireless signals as distance measurements. For a particular urban canyon environment, a computer model for satellite line-of-sight and signal strength expectations may be obtained and/or developed. The model parameters may include building height, road width, crossroads, satellite visibility and distribution of surrounding building occlusions, possible reflections and corresponding multi-path errors. The model may be self-learning, for example by using artificial intelligence, to incorporate data for each available location coordinate. A standard model may be used to aid analysis. An example of such a model would be an urban model, with 60% of all buildings being 20 stories of buildings and an average road width of 60 feet over a 5 mile radius. In the range of 5 to 20 miles from the center, 20% of all buildings are 8 stories with an average road width of 80 feet. In the range of 20 to 40 miles from the center, 35% of all buildings are single story buildings with an average road width of 100 feet. Each location coordinate of the city model may be improved by an update and refinement of the corresponding GPS-based information.
In operation 207, user dynamics of the GPS receiver are identified. A typical portable GPS receiver, such as the combined GPS receiver shown in fig. 1, may be used for mobile (dynamic) applications or fixed (static) applications. In one embodiment of the invention, the dynamic identification of the user is accomplished by user-provided input. In another embodiment, the information is from an initial GPS-based velocity result. In yet another embodiment, the user dynamics come from alternative radio location methods. Furthermore, user dynamics information can be determined by relying on previous results information or by using an urban canyon model and setting the specific application of the desired levels. For example, in an automatic vehicle location application, a standard urban model may include an expected average vehicle speed, such as 20 miles per hour over a 5 mile radius, 35 miles per hour over a 20 mile radius, and 50 miles per hour over a 40 mile radius. Each velocity result of the model may be updated. A database of maximum allowable speeds for particular streets in a particular city can also provide assistance.
In operation 209, the "dynamic" environment is further subdivided into "low", "medium", or "high" dynamic environments. The subdivision of the dynamic environment provides the speed at which the GPS receiver propagates information. In one embodiment of the present invention, the dynamic subdivision information of operation 207 is provided by a user input to the GPS receiver. In another embodiment, this information is determined from previous resulting information (e.g., velocity and acceleration) or by using an urban canyon model and a specific application that sets the desired level. For example, in vehicle tracking applications, additional sensor (e.g., speedometer and gyroscope) inputs may be used to provide initial velocity estimates or speed and/or direction information for further integration with GPS data.
Algorithm control parameter establishment
Fig. 6 is a flowchart outlining an operation included in the algorithmic control parameter setup process shown as operation 103 in fig. 4 in accordance with an embodiment of the present invention. An initial selection of a signal threshold is performed in operation 301. In one embodiment, this initial selection is performed based on the signal environment determined in operation 101 (shown in the flow chart of FIG. 5). The signal thresholds selected in operation 301 include a minimum signal-to-noise ratio (SNR) and a signal-to-interference ratio (SIR). For example, if san Francisco urban canyons are used as an example signal environment, the minimum signal-to-noise ratio and signal-to-interference ratio are set to 15.5 and 14.5dB, respectively. These thresholds are used for satellite measurement selection in operation 105.
In operation 303, a peak width parameter is set. These parameters are used for satellite selection and cross-correlation checks performed during the measurement selection and calculation process of operation 105. In one embodiment of the invention, the peak widths of all satellites are calculated at selected levels of signal-to-noise ratio and signal-to-interference ratio. For example, the peak width is calculated at a signal level 3dB below the punctual correlator signal level. The correlator function shown in fig. 3 represents the output of a point-like correlator at an instance with 8 half-chip relative time delays. The peak width of the peak of this particular correlator was calculated to be 1.02 half chips. In general, the peak width, peak width offset, and waveform of the correlator function may indicate the presence of multipath in the signal. E.g., the wider the peak, the greater the multipath error. Thus, the peak width mask may be used for satellite measurement selection at operation 105. Further, the waveform of the correlator function may indicate the presence of multiple signals. For example, the correlator function shown in fig. 3 indicates the presence of two earlier signals. Also, a slope with a 5-half chip time delay in the sample may indicate the presence of more than one signal. In most cases, the cross-correlation signal shows a wider peak. This makes the peak width measurement available for use in identifying potential cross-correlation signals in the measurement selection and calculation process (operation 105 in fig. 4).
In operation 305, a "strong" satellite is defined. A "strong" satellite is characterized by having satellite measurements that are minimally affected by multipath errors. In one embodiment of the invention, the parameters for "strong" satellite identification are satellite elevation, peak width offset, signal-to-noise ratio, signal-to-interference ratio, signal attenuation, and input signal strength. Taking the san Francisco city canyon as an example, the elevation mask can be set to 20, and the signal input strength can be set to-135 dB. Additionally, the signal input strength may be set to-130 dB for different urban environments, such as the Sengstore of Sichuan holly.
A satellite elevation signature defined independently of the "strong" signal is also set in operation 105. The mask may be used in the satellite selection step in the measurement selection and calculation process. In an "open sky" signal environment, the elevation mask may be set to a relatively low value, such as 5 °, since only small multipath errors are expected. However, in an "urban canyon" environment, the elevation mask is raised to 15 ° to avoid processing satellites that may be affected by multipath errors.
The flow of the state machine performing the error detection and isolation (FDI), offset adjustment (BA) and Sequential Measurement Optimization (SMO) algorithms is controlled by the parameters set by operation 307. For example, the order in which error detection and separation, offset adjustment, and successive measurement optimization calculations are performed may be varied based on signal environment characterization and the likelihood of cross-correlation false positives. For example, in an "open sky" environment, the likelihood of missing cross-correlations is small, and then offset adjustments may not be made at all, or may be made prior to error detection and separation. In another example, the height auxiliary measurements may or may not be included in the error detection and separation, offset adjustment, and continuous measurement optimization algorithms. An error estimate related to the altitude assistance measurement is set based on the signal environment characterization of operation 101. For example, for an "indoor" environment, altitude aiding may fail, or the altitude error may be set to a large value (e.g., 50m) to indicate a lack of confidence in the source of altitude information, which in some embodiments may be a terrain elevation database. In another example, the "strong" satellites defined by the control parameters may be different in the FDI, BA, and SMO algorithms.
Measurement selection and calculation
Fig. 7 is a flowchart outlining an operation included in the measurement selection and calculation process shown in operation 105 of fig. 4 in accordance with an embodiment of the present invention. The measurement selection and calculation process is used to pre-filter the position coordinate measurements and calculate the parameters needed to further process the GPS signals using the measurement processing method of the present invention.
In operation 401, the low elevation satellites are removed for further measurement processing. The set elevation mask may be established according to the signal environment characterization procedure shown in fig. 5 and the control parameters shown in fig. 6. In operation 403, an estimated input signal strength is calculated based on the measured output signal-to-noise ratio, the measurement integration period, and the doppler error. The input signal strength is then used to calculate the peak width shift and signal attenuation. The peak width offset is calculated as the difference between the measured peak width and the expected peak width for a satellite signal having a given input signal strength. Based on the signal environment characterization, in operation 405, weak signals are removed from further measurement processing using a signal-to-noise ratio or signal-to-interference ratio characterization code or estimated signal input strength or a combination of these three characterizations. In one embodiment, in the algorithm control parameter establishment process of FIG. 6, the signal threshold is set as described in operation 301.
The cross-correlation signal is detected and removed in operation 407. The cross-correlation signal generally exhibits a broad peak and a high noise-to-interference ratio (NIR). When a strong satellite signal and a weak satellite signal are cross-correlated, cross-correlation occurs in a high dynamic signal environment. In general, "indoor" and "urban canyon" environments produce many cross-correlation signals. The SNR, SIR and estimated input signal strength of the strong and weak satellite couplings can be detected (typically, detected) to obtain separation of the desired signals. Including the san francisco urban canyon terrain in a typical embodiment, an 18dB difference is searched. The cross-correlation is then checked by detecting the relative code phase and the doppler frequency of the strong and weak satellite coupling.
Under certain reception conditions, the waveform of the correlation peak will exhibit a two-peak signal with two main peaks. The waveform of fig. 3 illustrates such a dual peak signal. A dual peak signal is a special case of a multi-peak signal that is the result of a combination of multiple signals simultaneously incident on a GPS antenna. In operation 409, the correlation peak function, as shown in the waveform of fig. 3, is analyzed to determine the presence of a double peak. For example, the signal that received the largest correlation peak in fig. 3 is 1 microsecond later than the earlier signal. Because the reflected signal always travels a longer path than the direct signal, the main peak corresponds to the reflected signal and the earlier peak corresponds to the direct signal. In this example, correlation may be added to pseudorange (code phase) measurements to compute the presence of multipath signals. Typically, the correlation selects the earlier peak for the correct correlation output from an SPS satellite signal. In the case where the mobile SPS receiver performs some of the measurement processing techniques of the present invention (e.g., identifying the presence of dual peaks) and the SPS server performs other measurement processing techniques (e.g., FDI), the mobile receiver may issue an indication that a satellite has dual peaks. In another example, it would also send the relative positions of all identified peaks in the correlation peak function by reference to the instantaneous correlator (e.g., sample 8 in fig. 3). In yet another example, the mobile receiver may send a set of samples of the correlation peak function. The offset adjustment algorithm and/or SMO algorithm may use this data processing available time of arrival candidate to correct the pseudorange measurements described in operations 601 and 703. In operation 411, if a broad peak is detected and it does not belong to a double peak, then the signal is either corrected or removed from further measurement processing.
According to the signal environment, a weighting scheme is selected in operation 413. The weights represent a priori error uncertainty in the pseudorange measurements. For example, an error estimate of a weight of 0.1 is 10 meters. The weights may be derived from various parameters including signal-to-noise ratio, signal-to-interference ratio, noise-to-interference ratio, input signal strength, signal attenuation, elevation angle, measurement integration period, peak width offset, and the like. The weights for particular satellite measurements may be adjusted if dual peaks of the satellite signal are detected. The weights may also be adjusted if the measurement is not corrected at all or if the correction is too old (e.g., correction period greater than 30 seconds) to account for the presence of a selectively valid error. The error estimate may be improved by incorporating the available signal environment measurements in the system setup characterization as part of the signal environment characterization in operation 101. The weighting can also be improved if additional information is available. For example, in an "urban canyon" environment, the error estimate may be further refined by reflecting information external sources from the constantly updated urban computer model, such as the relative positions of surrounding buildings.
High levels of aiding may be used to improve the accuracy of the measurement processing algorithm in operation 415 in an outdoor signal environment. A high level of assistance improves the learned geometry and also provides the extra data necessary for the uncertain case. If an estimated altitude (e.g., average altitude of a cell site) is available, it may be used as an altitude assistance parameter. As the vertical terrain model improves, so will the height assistance. Algorithms for error detection and separation, offset adjustment and continuous measurement optimization will also benefit from accurate height assistance.
In an indoor signal environment, height assistance may be utilized if it is desired to generate coordinates without coordinate locations. In this case, the weighting reflects the uncertainty of the altitude measurement. The weight is defined as the inverse of the measurement error estimate. The height uncertainty may come from a city computer model. For example, if the indoor environment is a building 20 meters tall, a weight of 0.1 may be used. Altitude aiding may be used iteratively where the initial coordinates with great altitude uncertainty or no altitude aiding at all may be used as a lookup for the city computer model to obtain building altitude information and corresponding altitude uncertainty. In addition, the external source may provide information (i.e., a 10-story floor) for correcting the elevation from the terrain elevation database. In operation 417, clock assistance is applied. The clock uncertainty may come from a clock model that is based on the quality of an internal oscillator for the GPS receiver or the quality of an external timing signal, such as the signal used to set the GPS receiver time in a CDMA network. The clock model can be updated in real time by clock offsets and clock displacement estimates from the GPS pseudoranges and doppler measurements. If the timing information provided by the network is very accurate (e.g., to within 1 microsecond), such information may also assist the measurement processing method of the present invention by providing an additional degree of freedom.
Error detection and separation algorithm
Fig. 8 is a flowchart outlining an operation included in the error detection and separation process shown in operation 107 of fig. 4 in accordance with an embodiment of the present invention.
In one embodiment of the invention, the error detection and separation process is performed as part of a Receiver Automatic Integrity Monitoring (RAIM) function within the GPS receiver. In another embodiment of the present invention, the error detection and separation process is performed on the SPS server using the position information received by the GPS receiver. Various RAIM schemes have been proposed based on self-consistency checks among several available measurements. Some well-known error detection methods are range comparison, least squares residual, and consistency check, among others (see chapter v, volume two, global positioning system: theory and application, r.grover Brown). In one embodiment of the invention, the error detection and separation process is an extension of the error detection problem, in that the integrity system may also attempt to separate the wrong satellite measurements so that they can be removed from the navigation solution.
In operation 501, error detection and separation occurs. In one embodiment of the invention, a parity check method is used. (see navigation in Mark Sturza, "navigation System integrity monitoring with redundant measurements"). As part of error detection and separation, an F-test is performed to determine the reliability of the separation. Depending on the signal environment and the reliability of the separation, if a "strong" satellite is separated as a wrong satellite, offset adjustment and sequential measurement processing algorithms can be performed without any further error detection and separation processing. Likewise, if the error detection and separation process is highly assisted, again based on the height assistance parameters, and if the height measurements are separated, then the offset adjustment and continuous measurement processing algorithms may be performed without any further processing by the error detection and separation process. If, for example, the solution calculation occurs in an "open sky" environment with a good altitude estimate (i.e., with little uncertainty), and the altitude measurement is independent as a false measurement, then further error detection and separation processing may be stopped and the algorithms for offset adjustment and continuous measurement processing may be implemented.
Also in operation 503, an offset estimation in the independent measurements is performed. In one embodiment of the invention, the mathematical relationship of the residuals is solved using well-known a priori and a posteriori least squares, assuming that only the individual measurements are affected by an unknown offset in magnitude, the remaining measurements are perfect. The offset is then solved in the form of least squares. In one embodiment of the invention, if the magnitude of the offset exceeds a preselected threshold, then the independent measurement is declared a missing cross-correlation and appropriately removed from weighting.
Depending on the signal environment and the number of degrees of freedom, the offset or the division can be adjusted for the individual measurements. The number of degrees of freedom is defined as the difference between the total number of measurements and the number of unknown parameters to be resolved. The division by weight corresponds to removing the measured value from the solution, based on the weighting factor. In operation 505, an offset adjustment is performed on the measurement. In this method, the measured value is corrected with the offset estimated in operation 503. In one embodiment of the invention, the offset adjustment is made to the measured value only if the offset is positive.
In operation 507, new weights for the adjusted measurement values are calculated. The new weights may be based on signal environment, separation reliability, offset size, presence of multiple peaks in satellite measurements, and other factors.
In operation 509, a new solution and corresponding error estimate are calculated. In operation 511, it is determined whether any predefined emergency is caused. In one embodiment of the invention, the emergency condition includes HDOP exceeding the HDOP characterizing code, estimated level error exceeding a preselected threshold, unit variance below a preselected threshold or exceeding a second preselected threshold, variation of the solution before and after separation below a preselected threshold or exceeding a second preselected threshold, error separation failing the reliability test, degree of freedom below a preselected threshold, and other factors. If it is determined in operation 511 that no emergency is caused, the entire error detection and separation process is repeated from operation 501. Otherwise, the error detection and separation process ends.
Offset adjustment algorithm
Fig. 9 is a flowchart outlining an operation included in the offset adjustment process shown as operation 109 in fig. 4 in accordance with an embodiment of the present invention.
In one embodiment of the present invention, the offset adjustment algorithm as shown in FIG. 9 is similar to the offset estimation process with reference to operation 503 of FIG. 8. However, in the offset adjustment process of fig. 9, offset estimation is performed on any or any subset of the common received signals, and is not limited to only the detected individual erroneous measurements. In some instances, the selected subset may be the entire set of measurements. In one embodiment of the invention, the cross-correlation signal may not be included in the set, and "strong" satellites or "double peak" measurements may not be included. It should be noted that the definition of "strong" satellites in the context of the offset adjustment process may differ from the definition of "strong" satellites used in the context of the error detection and separation algorithm. In another embodiment, any or all of the pre-filtered measurements that are part of the operation 105 measurement selection and calculation of FIG. 4 may not be included in the set.
At operation 601, offset errors for a set of selected satellites are estimated. The offset estimate may or may not include an altitude measurement, depending on the signal environment and altitude aiding parameters. In operation 603, a maximum positive offset estimate is selected. In one embodiment of the invention, if height assistance is used, the offset of the height measurement may not be included in the selection. In another embodiment of the invention, the location of any multiple peaks in the correlation function may be selected as the offset estimate. In one example, the earliest recognizable peak is selected. In another example, any hill in the correlation function may be selected as the offset estimate. The selected measurements are then corrected with the offset estimate in operation 605.
In operation 607, the weights of the corrected measurements are adjusted to account for the offset adjustment. The new weights may be based on signal environment, offset size, size of corrected pseudorange residuals, algorithm control parameters, SNR, SIR, signal input strength in offset estimation correlation function samples, etc. A new solution and corresponding error estimate are calculated in operation 609.
In operation 611, it is determined whether any predefined emergency is caused. In one embodiment of the invention, the emergency condition includes the estimated level error proportional to the unit variance exceeding a preselected threshold, the unit variance being below the preselected threshold or exceeding a second preselected threshold, the variation of the solution before and after the offset adjustment being below the preselected threshold or exceeding the second preselected threshold, the number of degrees of freedom being below the preselected threshold, and other factors. If it is determined in operation 611 that no emergency is caused, the offset adjustment process is repeated from operation 601, otherwise, the offset adjustment process ends.
Sequential measurement optimization algorithm
Fig. 10 is a flowchart outlining an operation included in a Sequential Measurement Optimization (SMO) process as shown in operation 111 of fig. 4 in accordance with an embodiment of the present invention.
In one embodiment of the present invention, the sequential measurement optimization process is performed only if a specific condition, referred to as an "initial condition," is encountered. In operation 701, initial conditions of the sequential measurement optimization process are checked. Initial conditions include failure of error detection and separation reliability tests, or separation of "strong" satellites or altitude measurements with an erroneous detection and separation algorithm. A sequential measurement optimization process may also be initiated if an error statistic (e.g., estimated level error) exceeds a selected threshold based on the signal environment characterization. The initial conditions may also include any burstiness in error detection and separation and/or offset adjustment.
In another embodiment of the present invention, the set of control parameters of operation 307 in the algorithm control parameter establishment procedure may be set to force the sequential measurement optimization algorithm to always execute, rather than only when the initial conditions are present.
In operation 703, an offset is estimated for each selected satellite. In one embodiment, a set of selected satellite measurements may not include satellites that have been removed from the solution by a previous measurement processing step, such as through a measurement selection and calculation process, an error detection and separation process, or an offset adjustment process. "strong" satellites may also not be included in the set. Furthermore, the definition of "strong" satellites in the context of continuous measurement optimization may be different from the definition of "strong" satellites used in the context of error detection and separation or offset adjustment. In another embodiment, all satellites may be included in the set.
At operation 705, selected satellite measurements are processed according to a selected method. The selected method may be an offset adjustment technique, a weight adjustment technique, a time adjustment technique, a multipath mitigation technique, or some other measurement optimization technique. The offset adjustment technique may use the offset estimate calculated in operation 703 to correct the selected measurement and the adjustment weights to account for the correction. The weight adjustment technique can remove the weight of the satellite measurement value and reduce the influence of the measurement value on the overall solution. Time adjustment techniques may adjust satellite measurements in either direction (delayed or advanced time of arrival) to improve the solution. In another embodiment, only advancing the arrival time (e.g., reducing the arrival time) can be performed as part of the time adjustment technique. Multipath mitigation techniques may use a signal computer model to estimate the multipath error for a particular location and use this information in weighting the satellite measurements. In another embodiment, the inflection point of the correlation waveform (correlation function) is analyzed, which indicates the point of deviation from the ideal peak shape, and which may also indicate the point of combination of multiple signals. In yet another embodiment, multiple peaks at possible arrival times of the correlation waveform are analyzed.
In operation 707, a new solution and corresponding error statistics are calculated. In operation 709, a measurement value of the optimization error statistic is identified. In certain cases, optimization may correspond to minimization of error statistics. For example, the selected error statistics may be weighted root and squared posterior residuals. The error statistics are most selected based on signal environment characterization or "urban canyon" computer models, or previous information about the success of a particular method in a particular signal environment. Other available error statistics are unweighted root and square a posteriori residuals, weighted root and square a priori residuals, estimated level error, unit variance proportional to HDOP, and others.
In operation 711, it is determined whether there are available degrees of freedom for further sequential measurement optimization. If there are still degrees of freedom available, the sequential measurement optimization is repeated starting with operation 601, otherwise the sequential measurement optimization process ends. In one embodiment of the invention, the sequential measurement optimization process may be stopped if the resulting HDOP exceeds a preselected HDOP profile, or the resulting weighted HDOP exceeds a preselected weighted HDOP profile, if the selected error statistic is below a preselected threshold level, or if the current sequential measurement optimization iteration does not result in an improvement in the selected error statistic. Any emergency of FDI and/or offset stop procedures may be used to stop the SMO process.
Calculation of final solution and error estimate
Fig. 11 is a flowchart outlining an operation included in the final calculation and error estimation process shown as operation 103 in fig. 4 in accordance with an embodiment of the present invention.
In operation 801, a final solution and an error estimate are calculated. In one embodiment of the invention, the solution may include at least one of position, velocity, and timing information. Tests may also be performed to verify the solution. In one embodiment, the test is based on an environmental type, such as an "urban canyon" urban model. In another embodiment, in a vehicle tracking application, it is checked whether the solution is on a street location by comparing its location to its location on a digital map or other GIS (geographic information system) resource. The test checks whether the selected error statistic exceeds a preselected threshold. The test may also compare the solution to a previous solution or a series of previous solutions.
In operation 803, an error ellipse is calculated. The sizes of the semimajor and semiminor axes of the error ellipse and the azimuth angle can be analyzed according to the environment type. For example, under severe multipath conditions in an "urban canyon" environment, the azimuth of the error ellipse is generally perpendicular to the direction of the street. In other words, the semi-minor axis is coincident with the street direction.
In operation 805, the signal environment computer model is updated with the position solution information. The terrain elevation database may also be updated with an altitude solution for the outdoor signal environment.
Various methods of the present invention may be performed in part by a mobile SPS receiver, with the remainder being performed by a remote local SPS server. An example of a system operating with this method is shown in FIG. 12A, and an example of an SPS server is shown in FIG. 12B.
For purposes of explanation only, the system 900 of fig. 12A includes four cells 901, 902, 903, and 904 respectively served by cell base stations, hereinafter referred to as cell sites 901a, 902A, 903a, and 904 a. Each cell site provides two-way cellular radio communication using cell phones in the vicinity of the cell site, using well-known cellular telephone communication means. A typical cell phone may also include a mobile SPS receiver, such as receiver 901 b. Fig. 1 shows a specific example of a mobile unit 20 that may be constructed to implement an integrated mobile SPS receiver and cell phone 901 b. The cellular telephone in mobile unit 901b provides wireless communication to and from the cell site. The wireless communication may include voice data and SPS assistance data or SPS location information output as described above. For example, signal environment data may be provided to a cell phone, which may then be utilized by an SPS receiver to perform the measurement processing techniques of the present invention. This data may be obtained from a cell-based database, such as database 912a maintained by SPS server 912, and may then be used by the SPS receiver in unit 901b to perform the measurement processing techniques of the present invention within the SPS receiver. Typically, an SPS receiver will receive SPS signals and determine a correlation output of these signals for each satellite. Some of the measurement processing techniques of the present invention are then performed within the SPS receiver, others being performed by SPS servers, such as servers 914 and 912. Each mobile unit is connected to a server through a cell site and a mobile switching center, such as mobile switching centers 906 and 907, which in turn are connected to the server through a public switched telephone network 908 as shown in figure 12A. Thus, the pseudoranges and related outputs and other measurement processing outputs produced by the mobile SPS system 901b may be forwarded to the SPS server through the PSTN (public switched telephone network) of the cell site 901a and the mobile switching center 907 and a particular server, such as the SPS server 912. The SPS server then performs the remainder of the measurement processing techniques of the present invention to determine the final pseudoranges for the various satellites in view. Position calculations are also made using satellite ephemeris data received from a Wide Area Reference Network (WARN) 915. The determination of the last location by the SPS server then allows the server to provide this last location information to another system, such as application system 910, which in one embodiment may be a Public Safety Answering Point (PSAP). Yet another example of a system that may be utilized in the present invention is described in co-pending U.S. patent application serial No. 09/067,406 entitled "distributed satellite position system processing and utilization network", filed on 28/4/1998 by the inventors in Norman f. Examples of wide area reference networks are described in co-pending U.S. patent application serial No. 09/067,407 entitled "satellite positioning reference system and method", filed on 28/4/1998 by the inventors of Mark moegelein, Leonid Sheynblat, and Norman f. In addition to signal environment data, which may be stored in cell basis databases 912a and 914a, these databases may store average altitude as well as satellite related information, such as estimated doppler frequencies for satellites in view of various cell sites. An example of a cell-based database of this type is described in co-pending U.S. patent application serial No. 08/842,559 entitled "improved GPS receiver using a communication link", filed 1997, 4/15, by the inventor in Norman f.
It should be noted that a cellular-based communication system is a communication system having more than one transmitter, each serving a different geographical area predefined at any time in time. Typically, each transmitter is a wireless transmitter (e.g., a cell site) that serves a cell having a geographic radius of less than 20 miles, although the coverage area depends on the particular cellular system. There are many types of cellular communication systems such as cellular phones, PCS (personal communication system), SMR (dedicated mobile radio), one-way and two-way pager systems, RAM, ARDIS and wireless packet data systems. The different predefined geographical areas are generally referred to as cells, and many cells are grouped together to form a cellular service area, which are coupled to one or more cellular switching centers that provide connectivity to land-based telephone systems and/or networks. The service area is typically used for billing purposes. Thus, there may be cases where more than one cell within a service area is connected to a switching center. In addition, sometimes multiple cells within a service area are connected to different switching centers, especially in densely populated areas. A service area is generally defined as a set of geographically close cells. Another type of cellular system consistent with the above description is a satellite-based system in which the cellular base stations are typically earth orbiting satellites. In these systems, cell sectors and service areas move as a function of time. Examples of such systems include iridium, terrestrial star, Orbcomm, Odyssey systems.
Fig. 12B is an example of an SPS server according to an embodiment of the present invention. The server includes a data processing unit 951 coupled to a modem or other interface 953, to a modem or other interface 952, and to another modem or interface 954. In addition, a mass storage unit 955 is also coupled to the data processing unit 951. The optional GPS receiver may also be coupled 951 to the data processing unit. Mass memory 955 comprises an executable computer program for performing the processing operations of the present invention and also includes memory for storing cell-based information sources, such as cell-based database 912a, which associates locations within a cell site with specific signal environment data as described herein. Each modem or other interface provides an interface between the data processing unit 951 and the various components of the system 900 of fig. 12A. For example, a modem or other interface 953 provides a connection from between cellular switching centers, such as mobile switching center 907 and unit 951 in the case where the SPS receiver is directly coupled to the mobile switching center. As shown in fig. 12A, the link between the mobile switching centers is through the public switched telephone network, and thus interface 953 couples servers 912 and 914 to the public switched telephone network. In another embodiment, each cell site may comprise a server system, and thus interface 953 couples data processing unit 951 directly to a cell site, such as cell site 901 a. The interface 952 couples the unit 951 to other systems, such as the application system 910 shown in fig. 12A. Interface 954 couples unit 951 to a source of GPS signals, such as WARN 915 of fig. 12A.
In the above description, the system is described by measurement processing of navigation data in an SPS system, such as a GPS receiver. Although the present invention has been described with reference to particular embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (11)
1. A method of processing Satellite Positioning System (SPS) signals, said method comprising the steps of:
determining a first possible correlation peak by correlating received SPS signals with a reference signal, the received SPS signals including a first set of SPS signals from a first SPS satellite;
determining a second possible correlation peak by correlating the received SPS signal with a reference signal;
obtaining a measurement indicative of a time of arrival of the first set of SPS signals from one of the first and second possible correlation peaks.
2. The method of claim 1, wherein the second possible correlation peak follows the first possible correlation peak in time.
3. The method of claim 2 wherein said second possible correlation peak is generated from a reflected SPS signal.
4. The method of claim 3, wherein the measurement value representative of the time of arrival is from the first possible correlation peak.
5. The method as recited in claim 1, wherein said measurements representative of times of arrival of said first set of SPS signals are from operations on said first possible correlation peak and said second possible correlation peak.
6. The method of claim 1, wherein the first possible correlation peak and the second possible correlation peak are separated in time by less than one pseudorandom code period.
7. A method of processing Satellite Positioning System (SPS) signals, said method comprising the steps of:
determining signal environment data representative of a signal environment corresponding to a location at which an SPS receiver is located, wherein the signal environment data includes data representative of a multipath condition, an interference condition, or both a multipath condition and an interference condition of SPS signals in the vicinity of the location; and
determining how to process data representing SPS signals received by the SPS receiver from the signal environment data;
wherein said step of determining said signal environment data comprises determining at least one of a signal-to-noise ratio, a signal-to-interference ratio, a signal strength, or a peak width value of a cellular communication signal received by a cellular communication system, wherein said SPS receiver and said cellular communication system are coupled together and are part of a combined system.
8. A method of processing Satellite Positioning System (SPS) signals, said method comprising the steps of:
receiving a signal environment indicative of the manner in which SPS signals are propagated at a location at which the SPS receiver is located;
determining how to process data representing SPS signals received by the SPS receiver according to the signal environment;
wherein the signal environment is received from a cell-based information source.
9. A machine readable medium containing executable computer program instructions which, when executed by a digital processing system, cause the system to perform a method of measuring times of arrival of satellite signals received by a Satellite Positioning System (SPS) receiver, the method comprising the steps of:
characterizing a signal environment corresponding to a location at which the SPS receiver is located to produce environment data indicative of a manner in which SPS signals propagate locally to the location;
measuring times of arrival of respective satellite signals transmitted from two or more satellites; and
processing data representing the time of arrival with the environment data to produce a set of times of arrival from which position coordinates of the SPS receiver are calculated.
10. A machine readable medium containing executable computer program instructions which, when executed by a digital processing system, cause the system to perform a method of determining a position of a Satellite Positioning System (SPS) receiver, the method comprising the steps of:
determining at least a selected one of a peak width value of a correlation output of an SPS signal from an SPS satellite or a signal-to-interference ratio (SIR) of the SPS signal;
determining a position of said SPS receiver using said selected one of said peak width value or said SIR;
identifying one or more erroneous SPS signals; and
correcting the time of arrival measurements measured by the SPS receiver as a result of identifying one or more erroneous SPS signals.
11. A machine readable medium containing executable computer program instructions which, when executed by a digital processing system, cause the system to perform a method of determining a position of a Satellite Positioning System (SPS) receiver, the method comprising the steps of:
determining at least a selected one of a peak width value of a correlation output of an SPS signal from an SPS satellite or a signal-to-interference ratio (SIR) of the SPS signal; and
determining a position of said SPS receiver using said selected one of said peak width value or said SIR;
wherein time of arrival measurements are corrected with an offset adjustment based on one of the peak width value or the SIR.
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
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US09/241,334 | 1999-02-01 |
Publications (1)
Publication Number | Publication Date |
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HK1106583A true HK1106583A (en) | 2008-03-14 |
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