WO2019033464A1 - Mems coordinated seamless vehicle-mounted positioning method and system based on poi interaction - Google Patents

Abstract

The MEMS collaborative seamless vehicle positioning method based on POI interaction includes the following steps: designing POI structure oriented to navigation and positioning; extracting and generating POI; constructing integrated navigation and positioning system based on POI; combining basic functions and implementation approaches of geographic hotspot in GIS A MEMS collaborative seamless vehicle positioning method based on POI interaction is proposed to overcome the dilemma of error accumulation and navigation and positioning performance reduction in MEMS sensor and GNSS integration, which can effectively improve the efficiency of GNSS navigation and positioning in obstacle environments. The terminal is used for technical research and development, and its lines can be extended to mobile phones, PDAs, and various intelligent terminals and personal systems.

Description

MEMS collaborative seamless vehicle positioning method and system based on POI interaction Technical field

The present invention relates to the field of seamless navigation and positioning, and in particular to a MEMS collaborative seamless vehicle positioning method and system based on POI interaction.

Background technique

With the application of Beidou navigation and the demand for industry and industrial technology, the construction of a seamless navigation and positioning system has become a national strategic layout and the development of high-tech information technology. China has launched a major scientific project for the construction of the navigation system. The seamless navigation and positioning of various technologies and synergies is the focus and hotspot of current technology research and development.

Personal travel, vehicle navigation and positioning (including various key vehicles, such as cash transport vehicles, dangerous goods transport vehicles, school buses and security vehicles, etc.), and navigation and positioning of drones all pose realistic needs for seamless positioning.

Since its inception, the GNSS Global Positioning System has won the trust of customers with its remarkable features such as all-weather, high-precision, automation and high efficiency. With the continuous expansion of the application field of satellite navigation systems, many countries are stepping up the development of independent satellite navigation systems. The United States is implementing a GPS modernization plan. Russia has stepped up its efforts to restore and upgrade the overall performance of GLONASS. At the same time, the EU is also promoting the construction of the GALILEO system. China's Beidou satellite navigation system is building ahead as expected. At present, the application of BD/GPS/GLONASS has penetrated into many fields and has become an indispensable source of time and space information. It has been widely used in navigation, aerospace, measurement, motion vector monitoring, timing and many other aspects.

At the same time, the non-seamlessness of GNSS in applications also reveals the side effects of reliability and availability, such as satellite signal occlusion in challenging environments such as urban high-rise buildings, forest-intensive areas, alpine valleys and underground spaces. And interference, etc., formed the vulnerability of satellite navigation and positioning, affecting the comprehensive application of navigation and positioning. Over the years, in order to overcome these obstacles, people tend to use auxiliary sensors, such as odometers, compasses and inertial navigation, combined with satellite positioning to form an integrated positioning system; in urban environments, high-precision navigation maps can also be utilized. Some layer information, such as orientation, elevation and ground Punctuation, etc., combined with the positioning model to form an integrated positioning system, and obtain a variety of flexible positioning solutions in the obstacle environment.

In the prior art, the following two methods are common:

1. Integrated navigation and positioning mode of INS or MEMS and GNSS

Given that GNSS is not a seamless positioning and navigation system, the most straightforward addition is the introduction of an inertial positioning system (INS) integrated with the GNSS system, which uses a gyroscope and accelerometer mounted on the carrier to measure the transport. The carrier position method derives the position of the next point from the position of a known point based on the continuously measured carrier heading angle and speed, and thus continuously measures the current position of the moving body, so it is a deductive navigation method. The main advantages of the inertial navigation system are: (1) it does not depend on any external information, nor does it radiate energy to the outside, so it is an autonomous system that is well concealed and immune to external electromagnetic interference; (2) All-weather, global, and full-time work in the air, on the surface of the earth and even underwater; (3) can provide position, velocity, heading and attitude angle data, resulting in good continuity of navigation information and low noise; (4) data High update rate, short-term accuracy and stability. The disadvantages are: (1) because the navigation information is generated by integration, the positioning error increases with time, and the long-term accuracy is poor; (2) a long initialization time calibration is required before each use; (3) the price of the device depends on the device. The level varies, the middle and high end are more expensive; (4) the time information cannot be given.

Therefore, the integrated navigation technology with INS forming a variety of different combination modes has emerged one after another, and the advantages and disadvantages coexist. The GPS/INS integrated navigation and positioning technology relies on obvious advantages to become the mainstream technology of integrated navigation, and the two are efficient and effective. The combination can expand the advantages of each, reduce or overcome the shortcomings, use the real-time high-precision navigation and positioning information provided by GPS to guide the INS, and correct the error accumulated over time in real time. The customer GPS receiver cannot provide navigation information due to loss of lock or building occlusion. Difficulties; at the same time using INS real-time high-precision navigation information can improve the performance of GPS, so that the two benefits.

Due to the low cost and integration convenience of MEMS, and the development and improvement of strapdown inertial navigation theory, Widely used in recent years, known as the third generation of inertial navigation sensor materials, enabling the miniaturization of navigation systems; coupled with high-performance microcontrollers, such as ARM's DSP-enabled Cortex M4 series of microcontrollers, for modern navigation The system's miniaturization, low power consumption and high intelligence provide a good processor platform foundation. It must be pointed out that for low-cost MEMS devices, the drift of the positioning results is also obvious. For example, low-cost MEMS in mobile phones, there is obvious drift in a few seconds, and needs to be combined with other conditions (such as the position information of the digital compass). In order to control this error diffusion.

2. Dead reckoning algorithm

Dead reckoning (DR) is a method of estimating the position of the next moment by measuring the distance and azimuth of the movement under the condition that the current time position is known. Under the modern technical conditions, with the development of MEMS technology, the size, weight and cost of accelerometers, digital compasses and gyroscopes are greatly reduced, so that the dead reckoning can be easily applied in vehicle and pedestrian navigation. Because of the low-cost sensor integration and combination, its positioning system is also susceptible to error accumulation. It is necessary to consider combining other information that is easy to obtain to control the expansion and drift of system errors.

Therefore, it is necessary to design a new positioning method to solve the problems existing in the above prior art.

Summary of the invention

The object of the present invention is to overcome the shortcomings and deficiencies of the prior art, and to provide a MEMS collaborative seamless vehicle positioning method based on POI interaction.

Another object of the present invention is to provide a MEMS collaborative seamless vehicle positioning system based on POI interaction.

The object of the invention is achieved by the following technical solutions:

A MEMS collaborative seamless vehicle positioning method based on POI interaction includes the following steps:

(1) Design a POI structure for navigation and positioning:

The basic structural information contained in the POI structure is name, category, location, location accuracy indication, adjacent POI, road segment, granularity measurement, and identification features;

(2) Extraction and generation of POI:

Target specific task information descriptions, database topology layers, or map-related layers through repository support Relevant location information, after the feature information is extracted, according to the POI basic structure structure and representation symbol configuration oriented to the positioning requirement, an extended POI result supporting navigation positioning is formed;

(3) Construct a POI-based integrated navigation and positioning system:

This framework establishes the integrated positioning mode of GNSS/MEMS/POI. The positioning is based on satellite signal quality and inertial navigation threshold. The low-complexity adaptive intelligent switching model based on GNSS/MEMS/POI different positioning modes is developed. For the open area to the shadow area positioning mode switching; positioning needs to fully consider the satellite signal and positioning environment faced by the terminal, under normal circumstances will be based on the results of the Beidou / GPS positioning mode output; through the analysis of the positioning scene, the satellite The MEMS positioning is automatically enabled when the signal is disturbed or occluded, and the positioning mode is implemented to achieve adaptive switching and result evaluation according to the comprehensive evaluation of the positioning quality and the cumulative threshold of the inertial navigation error;

The MEMS inertial error accumulation threshold is evaluated. The MEMS inertial error cumulative threshold can be evaluated to ensure that the positioning error is within the allowable range. When the inertial error cumulative threshold exceeds the preset time T, the POI is automatically enabled and the POI is utilized. The spatial position information periodically calibrates the MEMS to ensure that the error accumulation does not exceed the allowable range, and the extended Kalman filter model is used, so that the behavior of the GNSS/MEMS/POI integrated positioning mode is optimally combined.

In step (2), the method and method for extracting the distribution pattern of the POI point are closely related to the road network structure, infrastructure and planning information, and the data sources thereof include a city and a road network database, and a navigation map associated with the positioning, according to These macroscopic information can obtain multiple distribution patterns of actual POI points in the region, thus laying a foundation for the basic structural model of POI.

The distribution pattern includes a random type, a sparse type, a region-intensive type, and a linear intensive type.

In the step (3), the shielding area includes, for example, a tunnel, a city, and a canyon.

In the step (3), the preset time is 50 seconds or 80 seconds.

Another object of the invention is achieved by the following technical solutions:

MEMS collaborative seamless in-vehicle positioning system based on POI interaction, including integrated positioning and analysis module, and GNSS module, MEMS module and POI integration module respectively connected with integrated positioning and analysis module a block, an output and a storage module, wherein the GNSS module transmits and receives signals through a GNSS antenna, and the MEMS module transmits and receives signals through a WiFi antenna or the Internet, and the POI integration module transmits and receives signals through GSM/CDMA/GPRS.

Compared with the prior art, the present invention has the following advantages and beneficial effects:

The present invention combines the basic functions and implementation approaches of geographic hotspots (POIs) in GIS, and proposes a MEMS collaborative seamless vehicle positioning method based on POI interaction, in order to overcome the error accumulation and navigation positioning performance degradation in MEMS sensor and GNSS integration. Difficulties can effectively improve the GNSS navigation and positioning efficiency under the obstacle environment (signal blocked or interfered with, etc.). The vehicle navigation terminal is used as an example for technical research and development, and the line can be extended to mobile phones, PDAs and various intelligent terminals. Personal system.

2. The technical scheme of the present invention utilizes geographic feature point (POI) information that experts and scholars have paid attention to in recent years, and combines the positioning model to conduct integrated positioning research to form a characteristic solution.

DRAWINGS

FIG. 1 is a flow chart of POI extraction and generation for navigation positioning.

Figure 2 is a flow chart of GNSS/MEMS/POI integrated positioning.

FIG. 3 is a schematic structural diagram of a MEMS collaborative seamless vehicle positioning system based on POI interaction according to the present invention.

Detailed ways

The present invention will be further described in detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

A MEMS collaborative seamless vehicle positioning method based on POI interaction includes the following steps:

(1) POI structure design for navigation and positioning

A single geography, or POI in the GIS field, contains basic structural information such as name, category, longitude latitude, and other points of interest in neighboring areas, such as hotel and hotel shops, which cannot be directly used for navigation and positioning. Expand the connotation of the POI so that it can carry the user's demand search, attribute view, space location call, route query and other functions, so the spatial position accuracy and attributes of the POI data The richness and clarity of expression directly affect the quality and usability of mobile positioning. The basic structure of POI in this framework is shown in Table 1:

Table 1: POI structure for navigation positioning

(2) Extraction and generation of POI

As shown in Figure 1, the extraction method and approach of the POI point distribution pattern are closely related to the road network structure, infrastructure and planning information. The most direct data sources include the city and road network database, and the navigation map associated with the location, according to these macro information. Various distribution patterns of actual POI points in the region, such as random type, sparse type, area-intensive type, and linear intensive type, can be obtained, thereby laying a foundation for the basic structural model of POI.

As shown in Figure 1, the extraction and generation of POI, through resource library support, positioning specific task information description, database topology layer, or related location information of map related layers, feature information, etc., after the extraction of POI basic structure according to the orientation-oriented requirements And the representation symbol configuration, forming an extended POI that supports navigation and positioning result.

(3) Construction of integrated navigation and positioning system based on POI

As shown in Figure 2, this framework establishes the integrated positioning mode of GNSS/MEMS/POI. The positioning is based on the satellite signal quality and the inertial navigation threshold value. The low computational complexity adaptive intelligence based on GNSS/MEMS/POI different positioning modes is developed. The model is switched, that is, the mode switching is performed for the open area to the shadow area (such as a tunnel, an urban canyon, etc.); the positioning needs to fully consider the satellite signal and the positioning environment faced by the terminal, and normally will be output in the Beidou/GPS positioning mode. The result is the benchmark; through the analysis of the positioning scene, the MEMS positioning is automatically enabled when the satellite signal is interfered or occluded, and the positioning mode is adaptively switched and the result is evaluated according to the comprehensive evaluation of the positioning quality and the cumulative threshold of the inertial navigation error. The evaluation of the accumulated threshold of MEMS inertial error can ensure that the error of the positioning result is within the allowable range. When the accumulated inertia error threshold exceeds a certain time, such as 50 seconds or 80 seconds, the system automatically enables the POI and utilizes the space of the POI. The position information periodically calibrates the MEMS to ensure that the error accumulation does not exceed the estimated range, and the extended Kalman filter model is used, so that the behavior of the GNSS/MEMS/POI integrated positioning mode is optimally combined.

As shown in Figure 3, the MEMS collaborative seamless vehicle positioning system based on POI interaction includes an integrated positioning and analysis module, and also includes a GNSS module, a MEMS module, a POI integration module, an output and a storage module respectively connected to the integrated positioning and analysis module ( That is, PNT), wherein the GNSS module transmits and receives signals through a GNSS antenna, and the MEMS module transmits and receives signals through a WiFi antenna or the Internet, and the POI integration module transmits and receives signals through GSM/CDMA/GPRS.

The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and combinations thereof may be made without departing from the spirit and scope of the invention. Simplifications should all be equivalent replacements and are included in the scope of the present invention.

Claims (6)

A MEMS collaborative seamless vehicle positioning method based on POI interaction, characterized in that it comprises the following steps: (1) Design a POI structure for navigation and positioning: The basic structural information contained in the POI structure is name, category, location, location accuracy indication, adjacent POI, road segment, granularity measurement, and identification features; (2) Extraction and generation of POI: Through the resource library support, the specific task information description, the database topology layer or the relevant location information of the map related layer are located. After the feature information is extracted, the POI basic structure structure and the representation symbol configuration are formed according to the positioning-oriented requirements, and an extension supporting the navigation positioning is formed. POI result; (3) Construct a POI-based integrated navigation and positioning system: This framework establishes the integrated positioning mode of GNSS/MEMS/POI. The positioning is based on satellite signal quality and inertial navigation threshold. The low-complexity adaptive intelligent switching model based on GNSS/MEMS/POI different positioning modes is developed. For the open area to the shadow area positioning mode switching; positioning needs to fully consider the satellite signal and positioning environment faced by the terminal, under normal circumstances will be based on the results of the Beidou / GPS positioning mode output; through the analysis of the positioning scene, the satellite The MEMS positioning is automatically enabled when the signal is disturbed or occluded, and the positioning mode is implemented to achieve adaptive switching and result evaluation according to the comprehensive evaluation of the positioning quality and the cumulative threshold of the inertial navigation error; The MEMS inertial error accumulation threshold is evaluated. When the inertial conduction error accumulation threshold exceeds the preset time T, the POI is automatically enabled, the spatial position information of the POI is used to periodically calibrate the MEMS, and the extended Kalman filter model is used. Therefore, the GNSS/MEMS/POI integrated positioning mode is optimally combined. The MEMS collaborative seamless vehicle positioning method based on POI interaction according to claim 1, wherein in the step (2), the POI point, a method and a method for extracting a distribution form thereof, and a road network structure, infrastructure and planning The information is closely related, and its data sources include urban and road network databases, and navigation maps associated with positioning. According to these macro information, various distribution patterns of actual POI points in the region can be obtained, thereby laying a foundation for the basic structural model of POI. The MEMS collaborative seamless vehicle positioning method based on POI interaction according to claim 2, wherein the distribution mode comprises a random type, a sparse type, a region-intensive type, and a linear dense type. The POI interaction-based MEMS collaborative seamless vehicle positioning method according to claim 1, wherein in the step (3), the shielding area comprises a tunnel, a city, and a canyon. The MEMS collaborative seamless vehicle positioning method based on POI interaction according to claim 1, wherein in step (3), the preset time is 50 seconds or 80 seconds. A POI interaction-based MEMS collaborative seamless vehicle positioning system for implementing a POI interaction-based MEMS collaborative seamless vehicle positioning method according to any of claims 1-5, characterized in that it comprises an integrated positioning and analysis module, The GNSS module, the MEMS module, the POI integration module, and the output and storage module respectively connected to the integrated positioning and analysis module, wherein the GNSS module transmits and receives signals through a GNSS antenna, and the MEMS module transmits and receives signals through a WiFi antenna or the Internet. The POI integration module transmits and receives signals via GSM/CDMA/GPRS.

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