High-precision positioning method and device

Abstract

The present application discloses a high-precision positioning method and device thereof, the method comprising: dividing N base stations communicating with tags into M base station groups, each base station group comprising k base stations, and then M=C k N , where N>k≥3,M>1; each of the M base station groups performs calculations to obtain the location coordinates of the tag; calculates the weight value of the location coordinates of each of the M base station groups; and clusters the weight values ​​of the location coordinates corresponding to the M base station groups, selects the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located, and weights them to obtain the location of the tag. This application effectively avoids the problem of fixed-point positioning jitter caused by RSSI fluctuations, and can also greatly improve the positioning accuracy of RSSI.

Description

Technical Field

[0001] The invention belongs to the technical field of high-precision positioning, and relates to a high-precision positioning method and a device thereof. Background Art

[0002] Among the two-dimensional and three-dimensional positioning methods for indoor positioning, the positioning method based on signal strength is widely used in large factories, parking lots, shopping malls and other scenarios due to its low deployment cost. It is a very important positioning method.

[0003] In the actual positioning system, multiple base stations are deployed in the environment for positioning. The existing positioning method based on signal strength (RSSI) generally selects the N base stations with the largest signal strength (sort the base station signal strength values ​​from large to small, and select the top N base stations to participate in the positioning solution, for example, N is greater than or equal to 3) to participate in the positioning solution. However, in this scheme, if a small number of base stations are selected, there may be large differences in the base stations participating in the positioning solution due to the RSSI fluctuations from the tag to the base station, resulting in positioning fluctuations and poor positioning stability, that is, the position determined at the same point has large jumps; if too many base stations are selected, the positioning fluctuations are relatively reduced, but the error of the solution is relatively large (if there are abnormal fluctuations, the jumps will definitely be larger; for normal fluctuations, more base stations are selected, and the signal strength of the base stations at a long distance will definitely be less accurate, so the positioning is inaccurate). Since the farther the base station is, the greater the signal strength fluctuations, if a base station (far away) that receives an incorrect signal strength is selected to participate in the positioning solution, the positioning error of the solution will be large. Therefore, in this positioning method based on signal strength, how to select appropriate base stations to participate in positioning solution and further improve positioning stability and positioning accuracy is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a high-precision positioning method and device thereof, which can effectively avoid the problem of fixed-point positioning jitter caused by RSSI fluctuations, and can also greatly improve the positioning accuracy of RSSI.

[0005] The present application discloses a high-precision positioning method, comprising:

[0006] Divide the N base stations that communicate with the tag into M base station groups, each base station group contains k base stations, and then Where N>k≥3,M>1;

[0007] Each of the M base station groups performs calculations and obtains the location coordinates of the tag;

[0008] Calculating a weight value of the position coordinates of each base station group in the M base station groups; and

[0009] The weight values ​​of the position coordinates corresponding to the M base station groups are clustered, and the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located are weighted to obtain the position of the tag.

[0010] In one embodiment, the step of calculating the weight value of the position coordinates of each base station group in the M base station groups further includes: the M base station groups use signal strength weights, distance weights, error weights, collinearity weights, and geometric precision factor weights for calculation, and the weight calculation formulas of each part are as follows:

[0011] w i =w rssi +w dis +w error +w Qals (k=3)

[0012] w i =w rssi +w dis +w error +w GDOP (k>3)

[0013] Among them, w i represents the sum of the weight values ​​corresponding to the k base stations in the i-th base station group, where:

[0014] w rssi =RSSi i / (RSSi1+RSSi2+…RSSi M )

[0015] w dis =Dist i / (Dist1+Dist2+…Dist M )

[0016] w error =Error i / (Dist1+Dist2+…Dist M )

[0017] w GDOP =Gdop i / (Gdop1+Gdop2+…Gdop M )

[0018] w Qals =Qals i / (Qals1+Qals2+…Qals M )

[0019] RSSi i =1 / |rssi i |

[0020] Dist i =1 / dist i

[0021] Error i =1 / error i

[0022] Gdop i =1 / gdop i

[0023] w rssi represents the signal strength weight of the i-th base station group, w dis represents the distance weight of the i-th base station group, w error represents the error weight of the ith base station group, w GDOP represents the geometric dilution of precision weight of the i-th base station group, w Qals represents the collinearity weight of the ith base station combination, rssi i is the sum of the signal strength values ​​from the label to the k base stations in the i-th base station group, dist i represents the sum of the distances from the position calculated by the i-th base station group to the k base stations, error i represents the sum of the distance errors between the position calculated by the i-th base station group and the k base stations in it, gdop i Represents the geometric precision factor of the i-th base station group, RSSi i Dist is the reciprocal of the absolute value of the sum of the signal strength values ​​from the label to the k base stations in the i-th base station group. i Indicates the reciprocal of the sum of the distances from the position calculated by the i-th base station group to the k base stations in it, Error i Qals represents the inverse of the sum of distance errors between the position calculated by the i-th base station group and the k base stations in it, i represents the collinearity of the ith base station group, Gdop i Represents the inverse of the geometric dilution of precision of the i-th base station group.

[0024] In one embodiment, k=3, and the collinearity of the i-th base station group is calculated using the following formula:

[0025]

[0026] Wherein, S is the area of ​​the triangle enclosed by the three base stations in the base station group, and l1, l2, and l3 are the three side lengths of the triangle respectively.

[0027] In one embodiment, k>3, the geometric precision factor is calculated using the following formula:

[0028]

[0029] Among them, (x, y, z) represents the coordinates of the label, (Ax i ,Ay i ,Az i ) represents the coordinates of the i-th base station in the base station group, d i Represents the measured distance from the tag to the i-th base station in the base station group.

[0030] In one embodiment, each of the M base station groups is solved using a weighted centroid algorithm and a least squares algorithm.

[0031] In one embodiment, clustering the weight values ​​of the M base station groups, selecting the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located for weighting, and obtaining the position of the label also includes:

[0032] Using a K-means algorithm to cluster the M base station groups into C cluster combinations;

[0033] Select M' base station groups of the cluster combination where the base station group with the largest weight value belongs;

[0034] The coordinates of the tag are obtained by performing positioning calculation based on the M' base station groups:

[0035]

[0036] Among them, B j (x, y, z) represents the solution coordinates corresponding to the jth base station group in the M' base station groups, w j Indicates the weight value corresponding to the jth base station group.

[0037] In one embodiment, before dividing the N base stations communicating with the tag into M base station groups, it also includes: sorting all N base stations communicating with the tag according to signal strength, if the maximum signal strength is greater than a predetermined threshold, the location coordinates of the tag are used as the coordinates of the base station corresponding to the maximum signal strength.

[0038] In one embodiment, if the maximum signal strength is less than or equal to a predetermined threshold, when N>3, the N base stations communicating with the tag are divided into M base station groups, each base station group includes k base stations, and then

[0039] The present application also discloses a high-precision positioning device, comprising:

[0040] The base station combination unit is used to divide the N base stations communicating with the tag into M base station groups, each base station group contains k base stations, and then Where N>k≥3,M>1;

[0041] A calculation unit, used to enable each of the M base station groups to perform calculations and obtain the position coordinates of the tag;

[0042] a weight calculation unit, configured to calculate a weight value of the position coordinates of each base station group in the M base station groups; and

[0043] The clustering unit is used to cluster the weight values ​​of the position coordinates corresponding to the M base station groups, select the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located, and weight them to obtain the position of the label.

[0044] The present application also discloses a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the steps in the method described above are implemented.

[0045] Compared with the prior art, the method of the present application has the following beneficial effects:

[0046] The present invention proposes a new high-precision positioning method, which arranges and combines the base stations involved in the positioning. Each combination can use a related solution algorithm (such as a weighted centroid algorithm or a least squares algorithm) to solve the location information of the tag, and then calculate the weight value corresponding to each base station combination. In order to further avoid the situation where the weight values ​​calculated for each base station combination are quite different due to RSSI fluctuations, it is proposed to cluster the weight values ​​corresponding to each base station combination, select the cluster where the maximum weight value of the base station combination is located, and weight the corresponding coordinates in the clustering results to solve the final coordinates, which can effectively avoid the problem of fixed-point positioning jitter caused by RSSI fluctuations, and can also greatly improve the positioning accuracy of RSSI. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a high-precision positioning method in one embodiment of the present invention.

[0048] Figure 2 It is a specific flow chart of a high-precision positioning method in one embodiment of the present invention.

[0049] Figure 3 These are the simulation results of the traditional positioning method and the positioning method of the present invention. DETAILED DESCRIPTION

[0050] In the following description, many technical details are provided to help readers better understand the present application. However, those skilled in the art can understand that even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the claims of the present application can be implemented.

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0052] In one embodiment of the present application, a high-precision positioning method is disclosed. Figure 1 is a flow chart of the positioning method. Figure 2 The specific process in an embodiment is shown. The positioning method includes the following steps:

[0053] Step 101: divide the N base stations communicating with the tag into M base station groups, each base station group includes k base stations, and then Where N>k≥3,M>1.

[0054] In one embodiment, before dividing the N base stations communicating with the tag into M base station groups, the method further includes: sorting all the N base stations communicating with the tag according to signal strength, and if the maximum signal strength is greater than a predetermined threshold, the tag is used as the base station coordinate corresponding to the maximum signal strength. If the maximum signal strength is less than or equal to the predetermined threshold; when N>3, M base station groups each including k base stations are selected from the N base stations communicating with the tag, where The predetermined threshold may be set to the RSSI value when the tag is one meter away from the base station.

[0055] It should be noted that signal strength values ​​beyond the communication radius will be considered as abnormal data and will not be included in the solution.

[0056] In step 102, each of the M base station groups performs a calculation and obtains the location coordinates of the tag. In one embodiment, each of the M base station groups can perform a positioning calculation using a weighted centroid algorithm and a least squares algorithm. It should be understood that the weighted centroid algorithm and the least squares algorithm are known to those skilled in the art, and are not described in detail in this embodiment.

[0057] In actual use, different calculation methods will be adopted according to the number of detected base stations. The least squares algorithm, weighted centroid algorithm, etc. can be used for positioning calculation (k>=3). Here, the least squares algorithm is selected as an example for corresponding description. The least squares method obtains the best estimate of the position of the tag to be located by minimizing the sum of squares of the errors.

[0058] Assume that the coordinates of the kth base station are known to be (Ax k ,Ay k ,Az k ), the distance from the tag to the kth base station is d k , the coordinates of the label are (x, y, z), then the following formula holds:

[0059]

[0060] Subtract the second equation from the first equation, the third equation from the second equation, and so on. Subtract the k-1th equation from the kth equation. This way, the unknown parameters in the system of equations can be eliminated: the following system of equations can be obtained:

[0061]

[0062] The matrix representation of this equation is as follows:

[0063] HX=B

[0064] X = (x, y, z) T

[0065]

[0066] Find the least squares solution for HX=B: X=(H T H) -1 H T B.

[0067] Step 103: Calculate the weight value of the position coordinates of each base station group in the M base station groups.

[0068] In one embodiment, the step of calculating the weight value of the positioning coordinates of each group in the M base station groups specifically includes the following method: the M base station groups use the following formula to calculate the weight value of each base station group:

[0069] w i =w rssi +w dis +w error +w Qals (k=3)

[0070] w i =w rssi +w dis +w error +w GDOP (k>3)

[0071] Among them, w i represents the sum of the weight values ​​corresponding to the k base stations in the i-th base station group, where:

[0072] w rssi =RSSi i / (RSSi1+RSSi2+…RSSi M )

[0073] w dis =Dist i / (Dist1+Dist2+…Dist M )

[0074] w error =Error i / (Dist1+Dist2+…Dist M )

[0075] w GDOP =Gdop i / (Gdop1+Gdop2+…Gdop M )

[0076] w Qals =Qals i / (Qals1+Qals2+…Qals M )

[0077] RSSi i =1 / |rssi i |

[0078] Dist i =1 / dist i

[0079] Error i =1 / error i

[0080] Gdop i =1 / gdop i

[0081] Among them, w rssi represents the signal strength weight of the i-th base station group, w dis represents the distance weight of the i-th base station group, w error represents the error weight of the ith base station group, w GDOP represents the geometric dilution of precision weight of the i-th base station group, w Qals represents the collinearity weight of the ith base station combination, rssi i is the sum of the signal strength values ​​from the label to the k base stations in the i-th base station group, dist i represents the sum of the distances from the position calculated by the i-th base station group to the k base stations, error i represents the sum of the distance errors between the position calculated by the i-th base station group and the k base stations in it, gdop i Represents the geometric precision factor of the i-th base station group, RSSi i Dist is the reciprocal of the absolute value of the sum of the signal strength values ​​from the label to the k base stations in the i-th base station group. i Indicates the reciprocal of the sum of the distances from the position calculated by the i-th base station group to the k base stations in it, Error iQals represents the inverse of the sum of distance errors between the position calculated by the i-th base station group and the k base stations in it, i represents the collinearity of the ith base station group, Gdop i Represents the inverse of the geometric dilution of precision of the i-th base station group.

[0082] It should be noted that when k=3, the weight value of the positioning coordinates of the base station group is related to the collinearity. When k>3, the weight value of the positioning coordinates of the base station group is related to the geometric precision factor. Collinearity is the degree of deviation between the actual connection between the base stations and the straight line when the base stations are collinear. It is used to measure the probability that the base stations are not in the same straight line. When the base stations are in a straight line, the collinearity is 0. The larger the collinearity, the less the base stations are in a straight line.

[0083] When k=3, the collinearity of the i-th base station group is calculated using the following formula:

[0084]

[0085] Wherein, S is the area of ​​the triangle enclosed by the three base stations in the base station group, and l1, l2, and l3 are the three side lengths of the triangle respectively.

[0086] When k>3, the geometric precision factor is calculated using the following formula:

[0087]

[0088] Among them, (Ax i ,Ay i ,Az i ) represents the coordinates of the i-th base station in the base station group, (x, y, z) represents the coordinates of the tag, d k Represents the measured distance from the tag to the i-th base station in the base station group.

[0089] Step 104 , clustering the weight values ​​of the position coordinates corresponding to the M base station groups, selecting the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located, and weighting them to obtain the position of the tag.

[0090] In one embodiment, clustering the weight values ​​of the M base station groups, and weighting the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located to obtain the position of the label specifically includes the following steps:

[0091] Using a K-means algorithm to cluster the M base station groups into C cluster combinations;

[0092] Select M' base station groups of the cluster combination where the base station group with the largest weight value belongs;

[0093] The coordinates of the tag are obtained by performing positioning calculation based on the M' base station groups:

[0094]

[0095] Among them, B j (x, y, z) represents the solution coordinates corresponding to the jth base station group in the M' base station groups, w j Indicates the weight value corresponding to the jth base station group.

[0096] In one embodiment, clustering using the K-means algorithm includes the following steps:

[0097] (1) arbitrarily select C objects from the M data objects (i.e., the weight values ​​corresponding to the M base station groups) as the initial clustering centers;

[0098] (2) Based on the mean of all objects in each cluster (central object), calculate the Euclidean distance between each object in the sample set and these central objects, and re-divide the corresponding objects based on the distance;

[0099] (3) Recalculate the mean (central object) of each (changed) cluster;

[0100] (4) Execute (2) to (3) repeatedly until each cluster no longer changes. Where C represents the number of clusters for clustering the weight value data corresponding to all base station combinations, which can be determined using the elbow rule.

[0101] Let M' represent the maximum weight value (i.e. max(w i )) The number of combinations in the cluster where the base station group is located, and the base station group in the cluster combination is selected to participate in the calculation of the final positioning coordinates. (x, y, z) represents the final positioning coordinates, B j (x, y, z) represents the solution coordinates corresponding to each base station group after clustering, w j represents the weight value corresponding to the jth base station group. Where M'<=M.

[0102]

[0103] In the existing RSSI positioning method, since the RSSI after filtering still has the problem of large fluctuations, when more base stations are deployed in the environment, it is difficult to determine the number of traditional base station selections. If fewer base stations are selected, the positioning stability is poor. If more base stations are selected, the farther the distance, the smaller the signal strength, the larger the ranging error, and the final positioning accuracy will be deteriorated due to the selection of a farther base station. The positioning method provided by the present invention sets a weight for the data of each group of base station combinations (as a reference for selecting to participate in coordinate calculation) and solves the weight, further clusters the weight, selects a type of base station combination with the largest weight in the clustering result to participate in the weighted solution of the final coordinates, and selects the correct base station combination based on the weight to participate in the positioning solution, thereby avoiding the problem of fixed-point jumping caused by the traditional algorithm selecting the largest N base stations to directly participate in the solution. By clustering the weights corresponding to each base station combination, the coordinates corresponding to the type of combination with the largest weight in the clustering result are selected for the weighted solution of the final coordinates, which can more effectively avoid the problem of fixed-point jumping and large positioning errors, and further improve the accuracy of the positioning result.

[0104] The above method solves the problems of poor positioning stability and poor positioning accuracy caused by the traditional method of selecting base stations. At the same time, the method is simple to operate and has strong practicality.

[0105] Based on this, the present invention conducts corresponding simulation experiments, arranges 9 base stations in a 10*10 environment, and simulates the motion trajectory in the environment. The distance converted by RSSI is simulated by adding Gaussian noise with a mean of 0 and a variance of 1 to the real distance value simulated by each base station. The simulation results are as follows: Figure 3 shown. Figure 3 The medium-dark gray is the positioning error of the least squares algorithm in the environment by selecting three base stations with the largest signal strength to participate in the solution, and the light gray is the positioning error of the algorithm proposed in this paper. The horizontal axis is the number of positioning points, and the vertical axis is the error value. It can be seen that the base station selection algorithm proposed in this paper significantly improves the accuracy and stability of positioning.

[0106] In addition, in order to test the accuracy and coordinate fluctuation of the fixed point, the fixed point (11.45, 2.6) in the simulation environment was tested 50 times to verify the mean square error and standard deviation of the coordinates, as shown in Table 1.

[0107] Mean square error Average coordinates Standard Deviation x Standard deviation y Traditional base station selection algorithm 2.95m (8.7,2.25) 1.99 0.92 The algorithm proposed in this paper 1.73m (10.37,2.61) 0.23 0.1

[0108] Table 1 Verification mean square error and standard deviation of fixed points

[0109] In another embodiment of the present application, a high-precision positioning device is disclosed, which includes: a base station combination unit, a solution unit, a weight calculation unit and a clustering unit. The base station combination unit is used to divide N base stations communicating with the tag into M base station groups, each base station group includes k base stations, and then Wherein N>k≥3,M>1. The solving unit is used to make each of the M base station groups solve and obtain the location coordinates of the tag. The weight calculation unit is used to calculate the weight value of the location coordinates of each of the M base station groups. The clustering unit is used to cluster the weight values ​​of the M base station groups, select the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located, and weight them to obtain the location of the tag.

[0110] Accordingly, other embodiments of the present application may also provide a computer-readable storage medium in which computer executable instructions are stored, and when the computer executable instructions are executed by the processor, the various method embodiments of the present application are implemented. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be a computer-readable instruction, a data structure, a module of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0111] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A high-precision positioning method, characterized in that: include: Divide the N base stations that communicate with the tag into M base station groups, each base station group contains k base stations, and then Where N>k≥3,M>1; Each of the M base station groups performs calculations to obtain the location coordinates of the tag; Calculating a weight value of the position coordinates of each base station group in the M base station groups; as well as The weight values ​​of the position coordinates corresponding to the M base station groups are clustered, and the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located are weighted to obtain the position of the tag.

2. The positioning method according to claim 1, characterized in that: The step of calculating the weight value of the position coordinates of each base station group in the M base station groups also includes: the M base station groups use signal strength weights, distance weights, error weights, collinearity weights and geometric precision factor weights for calculation, and the weight calculation formulas of each part are as follows: w i =w rssi +w dis +w error +w Qals (k=3) w i =w rssi +w dis +w error +w GDOP (k>3) Among them, w i represents the sum of the weight values ​​corresponding to the k base stations in the i-th base station group, where: w rssi =RSSi i / (RSSi1+RSSi2+…RSSi M ) w dis =Dist i / (Dist1+Dist2+…Dist M ) w error =Error i / (Dist1+Dist2+…Dist M ) In GDOP =Gdop i / (Gdop1+Gdop2+…Gdop M ) w Qals =Qals i / (Qals1+Qals2+…Qals M ) RSS i =1 / |rssi i | Dist i =1 / dist i Error i =1 / error i Gdop i =1 / gdop i w rssi represents the signal strength weight of the i-th base station group, w dis represents the distance weight of the i-th base station group, w error represents the error weight of the ith base station group, w GDOP represents the geometric dilution of precision weight of the i-th base station group, w Qals represents the collinearity weight of the ith base station combination, rssi i is the sum of the signal strength values ​​from the label to the k base stations in the i-th base station group, dist i represents the sum of the distances from the position calculated by the i-th base station group to the k base stations, error i represents the sum of the distance errors between the position calculated by the i-th base station group and the k base stations in it, gdop i Represents the geometric precision factor of the i-th base station group, RSSi i Dist is the reciprocal of the absolute value of the sum of the signal strength values ​​from the label to the k base stations in the i-th base station group. i Indicates the reciprocal of the sum of the distances from the position calculated by the i-th base station group to the k base stations in it, Error i Qals represents the inverse of the sum of distance errors between the position calculated by the i-th base station group and the k base stations in it, i represents the collinearity of the ith base station group, Gdop i Represents the inverse of the geometric dilution of precision of the i-th base station group.

3. The positioning method according to claim 2, characterized in that: k=3, the collinearity of the i-th base station group is calculated using the following formula: Wherein, S is the area of ​​the triangle enclosed by the three base stations in the base station group, and l1, l2, and l3 are the three side lengths of the triangle respectively.

4. The positioning method according to claim 2, characterized in that: k>3, the geometric precision factor is calculated using the following formula: Among them, (x, y, z) represents the coordinates of the label, (Ax i ,Ay i ,Az i ) represents the coordinates of the i-th base station in the base station group, d i Represents the measured distance from the tag to the i-th base station in the base station group.

5. The positioning method according to claim 1, characterized in that: Each of the M base station groups is solved by using a weighted centroid algorithm and a least squares algorithm.

6. The positioning method according to claim 1, characterized in that: Clustering the weight values ​​of the M base station groups, selecting the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located for weighting, and obtaining the position of the label, further comprising: Using a K-means algorithm to cluster the M base station groups into C cluster combinations; Select M' base station groups in the cluster combination where the base station group with the largest weight value belongs; The location coordinates of the tag are obtained by performing positioning calculation based on the M' base station groups: Among them, B j (x, y, z) represents the calculated position coordinates corresponding to the jth base station group in the M' base station groups, w j Indicates the weight value corresponding to the jth base station group.

7. The positioning method according to claim 1, characterized in that: Before dividing the N base stations communicating with the tag into M base station groups, the method also includes: sorting all N base stations communicating with the tag according to signal strength. If the maximum signal strength is greater than a predetermined threshold, the location coordinates of the tag are used as the coordinates of the base station corresponding to the maximum signal strength.

8. The positioning method according to claim 7, characterized in that: If the maximum signal strength is less than or equal to the predetermined threshold, when N>3, the N base stations communicating with the tag are divided into M base station groups, each base station group contains k base stations, and then 9. A high-precision positioning device, characterized in that: include: The base station combination unit is used to divide the N base stations communicating with the tag into M base station groups, each base station group contains k base stations, and then Where N>k≥3, M>1; A calculation unit, used to enable each of the M base station groups to perform calculations and obtain the position coordinates of the tag; A weight calculation unit, used to calculate a weight value of the position coordinates of each base station group in the M base station groups; as well as The clustering unit is used to cluster the weight values ​​of the position coordinates corresponding to the M base station groups, select the coordinates of all base station groups in the cluster combination where the base station group with the largest weight value is located, and weight them to obtain the position of the label.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps in the method according to any one of claims 1 to 8 are implemented.

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