TWI469669B - Method for estimating the position of cell tower - Google Patents

Description

Method for estimating the position of a cell base station

The present invention relates to a method for position estimation, and more particularly to a method for estimating the location of a cell base station based on global positioning satellite signals and cellular network signals to provide relevant positioning service information.

In recent years, with the development of diverse types of location-based services (LBS), wireless location technology has received attention and attention in related fields. The signal required for the positioning technology can be a Global Positioning System (GPS) signal, a reference signal of a wireless network system, or an auxiliary positioning signal of other systems. Due to the different content of signal measurement and different positioning methods, various positioning technologies in related fields are widely proposed.

In satellite-based positioning systems, GPS is the most well-known and widely used positioning system in a variety of fields. GPS transmits the positioning signal to the ground 24 hours a day through the 24 satellites deployed in space. The mobile device can receive the positioning signal and perform the three-dimensional spatial position calculation at any time in any place in the world by simply configuring the appropriate receiving device. GPS is mainly used to provide latitude and longitude coordinate positioning services for outdoor environments. The positioning accuracy is high, and the position information is only about 10 meters. However, since the positioning signals transmitted by GPS satellites are obscured by buildings, this technology cannot be used indoors. In addition, GPS positioning accuracy will be considerably reduced in the case of narrow urban streets or poor weather conditions in metropolitan areas.

On a cellular network-based positioning system, the most basic positioning technique is to use a cell tower, which is the Cell Global Identity (CGI) code of the base station. , to achieve two-dimensional spatial position solution. The advantage is that the complex position calculation is not required, and the technology can be used indoors. Since the positioning accuracy is directly dependent on the range covered by the cell base station, the metropolitan area is suburban, and the location information is from several hundred meters to Dozens of kilometers error. In addition, when the third party uses the positioning technology to provide relevant positioning service information, the real location information of the cell base station is not easily obtained from the cellular communication network system operator, so there is a bottleneck of its application.

In order to satisfy the seamless location-based service of the mobile device in different environments and solve the application bottleneck of the cell base station positioning technology, the present invention proposes a method for estimating the position of the cell base station.

The location estimation method of the present invention is for a hybrid wireless network based on satellite and cellular networks. At least one mobile training device is configured to obtain a plurality of training materials, wherein the training data includes a GPS location coordinate and a CGI code parameter and a signal strength value of the plurality of cell base stations, the plurality of cell base stations including a serving cell base station and at least A neighbor cell base station. A data computing server is used to perform the search, fusion, and location estimation of the training data, which is the location of the cell base station. A signal feature database calculates the fusion data of the server according to the data, updates or stores the data, and records the location information status.

The position estimation method of the example of the present invention defines a first reference coordinate according to the plurality of GPS position coordinates of the plurality of service category training data of the same CGI code parameter of the signal feature database, and the plurality of reference coordinates are based on the first reference coordinate The service category training data is divided into a plurality of clusters. Determining, according to the same CGI code parameter of the signal characteristic database, the plurality of signal strength values of the plurality of service class training data, a first reference signal strength value, a second reference signal strength value, and a third reference signal strength value Calculating a proximity value for each cluster based on the first reference signal strength value, selecting a cluster having a high close value from the plurality of clusters, and using the plurality of clusters based on the second reference signal strength value The plurality of GPS position coordinates of the service category training data and the plurality of signal strength values are used to calculate a first position information, and the first position information of the cell base station position is determined based on the third reference signal intensity value.

The position estimation method of the example of the present invention is based on the signal characteristic database The plurality of GPS location coordinates of the plurality of neighbor class training data of the same CGI code parameter defines a second reference coordinate, and the plurality of neighbor class training data is divided into a plurality of clusters based on the second reference coordinate. Determining, according to the same CGI code parameter of the signal characteristic database, the plurality of signal strength values of the plurality of neighbor class training data, a fourth reference signal strength value, a fifth reference signal strength value, and a sixth reference signal strength value Calculating a similar value for each cluster based on the fourth reference signal strength value, selecting a cluster having a high close value from the plurality of clusters, and training the plurality of neighbor categories of the cluster based on the fifth reference signal strength value The plurality of GPS position coordinates of the data and the plurality of signal intensity values are used to calculate a second position information, and the second position information of the cell base station position is determined based on the sixth reference signal intensity value.

The position estimating method of the example of the present invention combines the first orientation information, the second orientation information, the first location information, and the second location information, and uses a method to determine the location of the cell base station.

The above method of the present invention is a pure software architecture, which can be arranged in a physical machine through a program code. When the machine loads the code and executes it, the machine becomes the means for practicing the invention.

The advantages and disadvantages of the present invention will be readily understood by those of ordinary skill in the art in light of the appended claims.

1 is a schematic diagram showing a simplified architecture based on a satellite and a cellular network according to an embodiment of the present invention, including a plurality of GPS satellites (101, 102, 103), a plurality of cell base stations (104, 105, 106), A mobile training device 107 is, for example, a smart phone or a personal digital assistant (PDA), a data computing server 108, and a signal feature database 109. The data calculation server 108 and the signal feature database 109 are installed in the cloud. These GPS satellites transmit positioning signals to the ground around the clock. Each cell base station has a common control channel (CCH) that can continuously broadcast its signals in the cellular network to provide a unique CGI code parameter. It should be noted that the GPS satellite and the cell The number of base stations is not limited to the number shown in Fig. 1, and the number may vary in different embodiments without departing from the spirit of the invention.

2 is a schematic diagram of a data training architecture for location estimation according to an embodiment of the present invention. A mobile training device 107 is provided with a receiving unit 201, a data temporary storage unit 202, a data classification unit 203, a data encryption unit 204, and a standby device. Sub-database 205. The data computing server 108 is composed of a data decryption unit 207, a data fusion unit 208, and a position solving unit 209. The mobile training device 107 is coupled to the data computing server 108 via a cellular wireless network 206.

When the mobile training device 107 enters the outdoor target area of the hybrid network, the receiving unit 201 of the mobile training device 107 can acquire a plurality of training materials. A training material includes the following:

1. A GPS position coordinate. The working principle is as follows: a GPS receiver (not shown) in the receiving unit 201 of the mobile training device 107 detects a presence state of at least four GPS satellite signals, and a GPS satellite signal measures an arrival time (Time of The Arrival, ToA) value calculates the GPS position coordinates of the mobile training device 107 based on at least four ToA values.

2. CGI code parameters and signal strength values of a plurality of cell base stations. The receiving unit 201 of the mobile training device 107 detects a state of existence of signals of a plurality of cell base stations (104, 105, and 106 as shown in FIG. 1), and a cell base station signal can extract a CGI code parameter and measure A signal strength value. The plurality of cell base stations includes a serving cell base station (104 as shown in FIG. 1) and at least one neighbor cell base station (105, 106 as shown in FIG. 1).

As is known to those skilled in the art of GPS, the GPS position coordinates are typically updated every second, so the time stamp for signal detection is set to one second. At the time label, the receiving unit 201 of the mobile training device 107 can acquire the GPS position coordinates once and extract the CGI code parameters and measure the signal strength values twice. The data temporary storage unit 202 of the mobile training device 107 stores the plurality of training materials acquired, extracted and measured by the receiving unit 201, and transmits the stored training materials to the Data classification unit 203. The interval label for transmitting a batch of data is set to thirty seconds. It should be noted that, in the embodiment of the present invention, the set time stamp and the number of times are not limited to the foregoing number, and the number may be changed in different embodiments without departing from the spirit of the present invention. .

After the data classification unit 203 of the action training device 107 receives the training materials, class classification of the data is performed. In the category of the data, the training materials for the same CGI code parameter can be divided into a service category and a neighbor category. Before the training data is transmitted to the data computing server 108 via an application programming interface (API), the data encryption unit 204 is connected to the cellular wireless network 206 (shown as 104 in FIG. 1). And compressing and encrypting the training materials to form the encrypted training materials, and transmitting the encrypted training materials to the backup database 205 for storage. Based on the transmission characteristics of the wireless network, once the encrypted training data fails to be transmitted, the encrypted training data may be obtained from the backup database 205 to perform retransmission. After the data computing server 108 receives the encrypted training materials, the decrypting unit 207 of the data computing server 108 performs decompression and decryption on the encrypted training materials to form the decrypted training materials. Then, the data fusion unit 208 searches for a training data of the corresponding database by searching a signal feature database 109 to perform data fusion. Figure 3 below is mainly used to describe the steps of database search, data fusion and location estimation.

FIG. 3 is a schematic flowchart diagram of a data training architecture for location estimation according to an embodiment of the present invention. First, step 301 is executed to perform a search of a signal feature database 109 by using a CGI code parameter of the cell base station extracted as a key. In step 302, it is confirmed whether the CGI code parameter is a presence state.

1. If the CGI code parameter is in a presence state, the data fusion unit 208 acquires training data from the signal feature database 109 (step 303), performs the signal feature database training data, and integrates the training data. The data is merged into the signal signature database 109 (step 304). After receiving the merged data, the signal signature database 109 performs update storage, and records the location information of the cell base station as an update status (step 305).

2. If the CGI code parameter is in an unexisting state, the data fusion unit 208 directly transmits the training data to the signal feature database 109 (step 306). After the signal feature database 109 receives the training materials, the storage is performed, and the location information of the cell base station is recorded as an unknown state (step 307).

3. The signal signature database 109 examines the location information status of the cell base station. If the record of the location information is an unknown state, the signal profile database 109 immediately transmits the training data of the cell site to the location resolution unit 209 (step 308). If the record of the location information is in an update state, the signal profile database 109 periodically transmits the training data of the cell base station to the location resolution unit 209 (step 309). The location resolution unit 209 can establish a cluster based on the received training data and perform location estimation using an RF signal clustering algorithm (step 310). The RF signal is the received signal strength value. The location solving unit 209 returns the estimated location of the cell base station to the signal feature database 109. After receiving the location information, the signal feature database 109 performs storage, and records the location information status as one. Know the state (step 311).

4 is a latitude and longitude plot of position estimation in accordance with an embodiment of the present invention. After the position solving unit 209 of the data computing server 108 receives the plurality of training materials from the same CGI code parameter of the signal feature database 109, the plurality of training materials according to the same CGI code parameter The GPS position coordinates perform a latitude and longitude spatial distribution. Based on the category definition of the training data, the plurality of training materials can be divided into the plurality of service category training materials and the plurality of neighbor category training materials. FIG. 5 to FIG. 6 are mainly used to illustrate an implementation manner of the position estimating method of the present invention by using the plurality of service category training materials and the plurality of neighbor category training materials.

FIG. 5 is a schematic diagram showing the architecture of a location estimation by using a plurality of service category training data. First, step 501 is executed to determine the plurality of GPS position coordinates (X _i , Y _i ) of the plurality of service category training materials of the same CGI code parameter, where i=1, 2, . . . , N, using an algorithm definition A first reference coordinate, such as 40I shown in FIG. 4, in this embodiment, a centroid algorithm can be used, and the equation can be written as (X _ref1 , Y _ref1 )=(Σ _{i=1, ..., N} X _i /N, Σ _{i = 1,..., N} Y _i /N) However, the present invention is not limited thereto, and those skilled in the art should be able to understand other algorithms, such as a weighted center of gravity algorithm ( Both the Weight Centroid Algorithm and the Threshold Centroid Algorithm can be used to define the first reference coordinate.

Step 502 is performed to divide the plurality of service category training data of the same CGI code parameter into a plurality of clusters according to the first reference coordinate. In this embodiment, based on the first reference coordinate (X _ref1 , Y _{ref1 )} The X coordinate (ie, X _ref1 ) divides the plurality of service category training materials into two clusters, wherein the plurality of service category training materials of the kth (k=1, 2) clusters can be represented by the set G _k . However, the present invention is not limited thereto, and the Y coordinate (ie, Y _ref1 ) of the first reference coordinate (X _ref1 , Y _ref1 ) may also be used to perform cluster division of the plurality of service category training materials. It should be noted that the number of clusters is not limited thereto, and the number may vary in different embodiments without departing from the spirit of the invention.

In step 503, the plurality of signal strength values RSS _i of the plurality of service class training data of the same CGI code parameter, wherein i=1, 2, . . . , N, define a first reference signal strength value, and The second reference signal strength value and a third reference signal strength value. In this embodiment, a centroid algorithm can be used to define a first reference signal strength value, and the equation can be written as RSS _ref1 = Σ _{i = 1, ..., N} RSS _i /N, but the invention is not limited Here, those skilled in the art should be able to understand other algorithms, such as a weighted center of gravity algorithm and a threshold value center of gravity algorithm can be used to define the first reference signal strength value. In this embodiment, a second reference signal strength value can be defined using the lowest signal strength value, the equation of which can be written as RSS _ref2 = min{RSS _i }. In this embodiment, a third signal strength value can be defined using the highest signal strength value, and the equation can be written as RSS _ref3 = max{RSS _i }.

Step 504 is performed to calculate a proximity value for each cluster according to the first reference signal strength value, which is implemented by: assuming that G _k , k=1 or 2, including n _k service category training materials, Each service category training data has a signal strength measurement value. If the signal strength measurement value is less than RSS _ref1 , there is no similar value. If the signal strength measurement value is greater than or equal to RSS _ref1 , a similarity value is calculated, and then, n _k The similar values are summed to obtain a sum similarity value, and a cluster having a high sum close value is selected from the k clusters.

In step 505, a first location information is calculated according to the plurality of service class training data of the cluster with high total similar values, and the implementation manner is: a G _k , k=1 or 2, including n _k service categories Training data, each service category training data has a GPS position coordinate and a signal strength measurement value. If the signal strength measurement value is equal to RSS _ref2 , the weight value is W _f =1, and if the signal strength measurement value is not equal to RSS _ref2 , Then calculate a weight value W _f , and then use a weight averaging algorithm to calculate a first position information, as shown in FIG. 4 402 , the equation can be written as X _ser = Σ _{f = 1, ..., nk} (W _f ×X _f )/Σ _f=1,...,nk W _f

Y _ser =Σ _f=1,...,nk (W _f ×Y _f )/Σ _f=1,...,nk W _f Finally, the position of the cell base station is determined according to the third reference signal intensity value. A first orientation information (step 506).

FIG. 6 is a schematic diagram showing the architecture of a location estimation by using the plurality of neighbor category training data. First, step 601 is executed to determine the plurality of GPS position coordinates (X _j , Y _j ) of the plurality of neighbor class training data of the same CGI code parameter, where j=1, 2, . . . , M, using an algorithm definition A second reference coordinate, such as 403 shown in FIG. 4, in this embodiment, a center of gravity algorithm can be used, the equation of which can be written as (X _ref2 , Y _ref2 )=(Σ _{j=1,..., M} X _j /M, Σ _j=1,...,N Y _j /M) However, the present invention is not limited thereto, and those skilled in the art should be able to understand other algorithms, such as weighted center of gravity algorithm and threshold value center of gravity algorithm. Both can be used to define the second reference coordinate.

Step 602 is performed to divide the plurality of neighbor class training data of the same CGI code parameter into a plurality of clusters according to the second reference coordinate. In this embodiment, the X based on the second reference coordinate (X _ref2 , Y _ref2 ) The coordinates (ie, X _ref2 ) divide the plurality of neighbor category training data into two clusters, wherein the plurality of neighbor category training materials of the kth (k=1, 2) clusters may be represented by the set H _k , but the present invention Without limitation, the Y coordinate (ie, Y _ref2 ) of the second reference coordinate (X _ref2 , Y _ref2 ) may also be used to perform cluster division of the plurality of neighbor category training materials. It should be noted that the number of clusters is not limited thereto, and the number may vary in different embodiments without departing from the spirit of the invention.

In step 603, the complex signal strength values RSS _j of the plurality of neighbor class training data of the same CGI code parameter, where j=1, 2, . . . , M, use a method to define a fourth reference signal strength. a value, a fifth reference signal strength value, and a sixth reference signal strength value. In this embodiment, a center of gravity algorithm can be used to define a fourth reference signal strength value, and the equation can be written as RSS _ref4 = Σ _{j = 1, ..., M} RSS _j / M, but the invention is not limited Here, those skilled in the art should be able to understand other algorithms, such as a weighted center of gravity algorithm and a threshold value center of gravity algorithm can be used to define the fourth reference signal strength value. In this embodiment, a fifth reference signal strength value can be defined using the lowest signal strength value, the equation of which can be written as RSS _ref5 = min{RSS _j }. In this embodiment, a sixth signal strength value can be defined using the highest signal strength value, and the equation can be written as RSS _ref6 = max {RSS _j }.

Step 604 is performed to calculate a proximity value for each cluster according to the fourth reference signal strength value, which is implemented by: assuming that H _k , k=1 or 2, including m _k neighbor class training materials, Each neighbor class training data has a signal strength measurement value. If the signal strength measurement value is less than RSS _ref4 , there is no similar value. If the signal strength measurement value is greater than or equal to RSS _ref4 , a similarity value is calculated, and then, m _k The similar values are summed to obtain a sum similarity value, and a cluster having a high sum close value is selected from the k clusters.

In step 605, a second location information is calculated according to the plurality of neighbor class training data of the cluster with high total similar values, and the implementation manner is: assuming that H _k , k=1 or 2, including m _k neighbor categories Training data, each neighbor category training data has a GPS position coordinate and a signal strength measurement value. If the signal strength measurement value is equal to RSS _ref5 , the weight value is W _f =1, and if the signal strength measurement value is not equal to RSS _ref5 , Then calculate a weight value W _f , and then use a weighted average algorithm to calculate a second position information, as shown in FIG. 4 , 404, the equation can be written as X _nei = Σ _{f = 1, ..., mk} (W _f ×X _f )/Σ _f=1,...,mk W _f

Y _nei =Σ _f=1,...,mk (W _f ×Y _f )/Σ _f=1,...,mk W _f Finally, the position of the cell base station is determined according to the sixth reference signal intensity value A second orientation information (step 606).

FIG. 7 is a schematic structural flow diagram of determining a location of a location estimation method according to an embodiment of the present invention. The first position information and the second position information obtained by the position calculating unit 209 of the data computing server 108 and the first position information and the second position information are calculated, and a conditional method is used to determine The location of the cell base station, which performs the following steps: First, step 701 is executed to determine whether the RSS _ref4 value is an acquired state. If the RSS _ref4 value is determined to be an _unobtained state, the first location information is used to determine the cell. The location of the base station (step 702); if it is determined that the RSS _ref4 value is the acquired state, the process proceeds to step 703.

In step 703, it is determined whether the RSS _ref1 value is smaller than the RSS _ref4 value, and if the RSS _ref1 value is determined to be smaller than the RSS _ref4 value, the second location information is used to determine the location of the cell base station (step 704); If it is determined that the RSS _ref1 value is not less than the RSS _ref4 value, the process proceeds to step 705.

In step 705, it is determined whether the first orientation information and the second orientation information are the same orientation. If the determination is the same orientation, the first location information is used to determine the location of the cell base station (step 706); If it is determined that the orientation is different, the process proceeds to step 707.

In step 707, an interval is defined _using the X coordinate (ie, X _ref1 ) of the first reference coordinate (X _ref1 , Y _ref1 ) and the X coordinate (ie, X _ref2 ) of the second reference coordinate (X _ref2 , Y _ref2 ). Step 708 is continued to determine whether the first orientation information and the second orientation information are located in the interval. If it is determined to be located in the interval, the first location information is used to determine the location of the cell base station (step 709). As shown in FIG. 4, if it is determined that the interval is not located, an average value is calculated based on the first location information and the second location information to determine the location of the cell base station (step 710).

The method of the present invention, or a specific system unit, or a part thereof, is a pure software architecture, and can be disposed on a physical medium such as a hard disk, a CD, or any electronic device (such as a smart phone or a computer) through a program code. The readable storage medium), when the machine loads the code and executes (eg, the smart phone is loaded and executed), the machine becomes the device for practicing the present invention. The method and apparatus of the present invention can also be transmitted by a transmission medium such as a cable, an optical fiber, or any transmission type, and the code is received, loaded, and executed by a machine (such as a smart phone). The machine becomes the device for carrying out the invention.

The detailed description of the preferred embodiments of the present invention is intended to be limited to the scope of the invention, and is not intended to limit the scope of the invention. The patent scope of this case.

101, 102, 103‧‧‧ GPS satellites

104, 105, 106‧‧‧ cell base station

107‧‧‧Action training device

108‧‧‧Data computing server

109‧‧‧Signal signature database

201‧‧‧ receiving unit

202‧‧‧data temporary storage unit

203‧‧‧Information Classification Unit

204‧‧‧ Data Encryption Unit

205‧‧ ‧Backup database

206‧‧‧Hive Wireless Network

207‧‧‧Information decryption unit

208‧‧‧Information integration unit

209‧‧‧ position solving unit

401‧‧‧ first reference coordinates

402‧‧‧First location information

403‧‧‧second reference coordinates

404‧‧‧Second location information

405‧‧‧ Estimated cell base station position

301~311‧‧‧Steps

501~506‧‧‧Steps

601~606‧‧‧Steps

701~710‧‧‧Steps

FIG. 1 is a schematic diagram showing a simplified architecture of a satellite and a cellular network according to an embodiment of the present invention; FIG. 2 is a schematic diagram of a data training architecture for location estimation according to an embodiment of the present invention; FIG. FIG. 4 is a longitude latitude plot of position estimation according to an embodiment of the present invention; FIG. 5 is a position estimation by using the plurality of service category training data. Schematic diagram of the architecture flow; FIG. 6 is a schematic diagram showing the architecture of the location estimation by using the plurality of neighbor category training data; FIG. 7 is a schematic diagram of the location estimation method according to the embodiment of the present invention. Schematic diagram of the infrastructure flow.

107‧‧‧Action training device

108‧‧‧Data computing server

109‧‧‧Signal signature database

201‧‧‧ receiving unit

202‧‧‧data temporary storage unit

203‧‧‧Information Classification Unit

204‧‧‧ Data Encryption Unit

205‧‧‧Backup database

206‧‧‧Hive Wireless Network

207‧‧‧Information decryption unit

208‧‧‧Data Fusion Unit

209‧‧‧ position solving unit

Claims (10)

A location estimation method for a hybrid wireless network based on a satellite and a cellular network, comprising the steps of: performing a first orientation information and a first based on the plurality of service category training data of the same CGI code parameter. Estimating a position information and a first reference signal strength value; estimating a second position information and a second position information and a fourth reference signal strength value according to the plurality of neighbor class training data of the same CGI code parameter; And determining the location of the cell base station in combination with the first orientation information, the first location information, the first reference signal strength value, the second orientation information, the second location information, and the fourth reference signal strength value. The method for estimating a position according to claim 1, wherein the first orientation information and the first location information and the first reference signal strength value are estimated, comprising the steps of: training data according to the plurality of service categories; The plurality of GPS position coordinates define a first reference coordinate by using an algorithm; the plurality of service category training data is divided into a plurality of clusters according to the first reference coordinate; and the plurality of signal strength values of the training data according to the plurality of service categories Defining a first reference signal strength value, a second reference signal strength value, and a third reference signal strength value; calculating a probability value for each cluster according to the first reference signal strength value, and from the plurality of The cluster selects a cluster having a high sum of similar values; calculating a first location information based on the second reference signal strength value according to the plurality of service category training data of the cluster having a high total similar value; and according to the third reference signal The intensity value is used to determine the first orientation information of one of the cell base station positions. The position estimating method according to claim 2, further comprising using a gravity center algorithm to calculate the first reference coordinate. The method of position estimation according to claim 2, further comprising: using a centroid algorithm to calculate a first reference signal strength value, and using a lowest signal strength value to define a second reference signal strength value to be defined using the highest signal strength value The third reference signal strength value. The method for estimating a position as described in claim 2, further comprising using a weighted average algorithm to calculate a first location information. The method for estimating a location according to claim 1, wherein the second orientation information and the second location information and the fourth reference signal strength value are estimated to include the following steps: training data according to the plurality of neighbor categories The plurality of GPS position coordinates define a second reference coordinate by using an algorithm; and the plurality of neighbor class training data is divided into a plurality of clusters according to the second reference coordinate; the plurality of signal strength values of the training data according to the plurality of neighbor categories Defining a fourth reference signal strength value, a fifth reference signal strength value, and a sixth reference signal strength value; calculating a probability value for each cluster according to the fourth reference signal strength value, and from the plurality of The cluster selects a cluster having a high sum of similar values; calculating a second location information based on the fifth reference signal strength value according to the plurality of neighbor class training data of the cluster having a high total similar value; and according to the sixth reference signal The intensity value is used to determine the second orientation information of one of the cell base station positions. The position estimating method of claim 6, wherein the method further comprises using a centroid algorithm to calculate the second reference coordinate. The position estimating method according to claim 6, wherein the method further comprises: using a center of gravity algorithm to calculate a fourth reference signal strength value, and using a lowest signal strength value to define a fifth reference signal strength value to be defined using the highest signal strength value. The sixth reference signal strength value. The location estimation method according to claim 6, wherein the method further comprises using a weighted average algorithm to calculate a second location information. The method for estimating a position according to claim 1, wherein the first orientation information, the first location information, the first reference signal strength value, the second orientation information, the second location information, and the first Determining the position of the cell base station by using four reference signal strength values, comprising the steps of: determining the location of the cell base station if the fourth reference signal strength value is not obtained; If the reference signal strength value is obtained, and the first reference signal strength value is less than the fourth reference signal strength value, the second location information is used to determine the location of the cell base station; if the fourth reference signal strength value is obtained And the first reference signal strength value is not less than the fourth reference signal strength value, and the first orientation information and the second orientation information are the same orientation, and the first location information is used to determine the cell base station Position: defining an interval according to the first reference coordinate and the second reference coordinate; if the fourth reference signal strength value is obtained, and the first reference signal strength value Not less than the fourth reference signal strength value, and the first orientation information and the second orientation information are located in the interval, the first location information is used to determine the location of the cell base station; and if the fourth reference signal If the intensity value is obtained, and the first reference signal strength value is not less than the fourth reference signal strength value, and the first orientation information and the second orientation information are not located in the interval, the first location information is used. The second position information is used to calculate an average to determine the location of the cell base station.