Enhanced positioning method based on TDOA complex scene

Abstract

The invention discloses an enhanced positioning method based on a TDOA complex scene, and the method comprises the steps: S1, carrying out the framing according to the positioning data received by a base station, and obtaining a positioning data frame of a plurality of base stations when positioning information is transmitted once by a positioning tag; s2, judging whether the number of arrived base stations meets the requirement or not; s3, selecting a reference base station; s4, the reference base stations are combined in pairs and then serve as benchmarks in sequence to traverse other base stations in the positioning data frame for positioning calculation, a candidate data set under each combination benchmark is obtained, and a hidden state data set is obtained; s5, combining the hidden state data sets of the previous frame and the current positioning frame, calculating state transition probabilities of elements of the current positioning data set, and screening the state transition probabilities to obtain a positioning result sequence; s6, obtaining a final positioning result; the positioning data frame in the step S1 comprises base station coordinate information and timestamp information when the positioning information reaches the base station; the method is combined with historical positions to improve positioning stability.

Description

[0001] This application is a divisional application of the Chinese invention patent application with application number "202310287564.7", application date "March 22, 2023", and invention name "Enhanced positioning method in complex scenes based on TDOA". Technical Field

[0002] The present invention belongs to the field of wireless positioning technology, and in particular relates to an enhanced positioning method based on TDOA in complex scenarios. Background Art

[0003] UWB technology transmits data through extremely narrow pulses rather than traditional carriers, resulting in extremely high data transmission speeds. In addition, UWB technology has the advantages of low system complexity, high information security, and strong resistance to multipath fading, making it a highlight in the field of wireless positioning.

[0004] The accuracy of TDOA-based UWB positioning is primarily affected by wireless signal propagation and base station topology. For example, issues such as signal reflection, multipath propagation, and non-line-of-sight (NLOS) can all produce different measurement errors, affecting the stability and accuracy of positioning results. Furthermore, the number of base stations and their topology can also affect the accuracy of the TDOA positioning algorithm. A good topology means that base stations are not concentrated in one area but are evenly distributed across different azimuths. Therefore, there are certain requirements for the number and arrangement of base stations.

[0005] Currently, the main means to improve TDOA's positioning performance in complex scenarios include: performing non-line-of-sight identification on the original positioning data to eliminate NLOS data and performing topological screening on the received base station data to ensure positioning stability. These data processing undoubtedly increases the complexity of the algorithm and the real-time performance of the back-end calculation, while also resulting in a reduction in the amount of positioning data and affecting the smoothness of the positioning trajectory.

[0006] Chinese patent document CN109041207A discloses a precise positioning system based on BIM technology for virtual reality and augmented reality. The system includes: a UWB signal transmission module; multiple UWB signal receiving base stations; a data processing module that determines the position of the UWB signal transmission module relative to N UWB signal receiving base stations using a TDOA algorithm; a data storage module; multiple cameras evenly arranged within the construction tunnel; a video monitoring module that receives video information captured by each camera; a BIM module with a preset electronic map of the construction tunnel interior. The BIM module converts the stored position information of the UWB signal transmission module relative to the N UWB signal receiving base stations into position coordinates on the electronic map of the construction tunnel interior; and a virtual reality or augmented reality display device. However, this positioning system fails to address the difficulties of non-line-of-sight wireless signal identification and base station screening.

[0007] Chinese patent document (CN113030859A) discloses a UWB indoor positioning method based on time division multiple access. The steps include: setting up N base stations BSi within the positioning area; the terminal MS transmits a UWB signal once and records the transmission timestamp TMST; after receiving the UWB signal, the base station BSi records the reception timestamp TBSR1(i); the central base station transmits a UWB signal once and records the transmission timestamp TBST; after receiving the UWB signal, the positioning base station records the reception timestamp TBSR2(i); and parameterizing the positioning solution matrix to obtain the coordinates of the terminal MS. This UWB indoor positioning method utilizes N base stations BSi for combined positioning, effectively enhancing the positioning accuracy of the terminal MS and achieving load balancing of the base stations. It also utilizes two UWB signal transmissions to locate the terminal MS using the TDOA positioning method, reducing the deployment cost of the UWB indoor positioning system. However, this positioning method still suffers from unstable positioning. Summary of the Invention

[0008] In response to the above problems, the present invention provides an enhanced positioning method based on TDOA in complex scenarios, which can improve the positioning performance of UWB in complex scenarios, solve the difficulties of non-line-of-sight identification of wireless signals and base station screening, and at the same time combine historical location information to improve the stability of positioning.

[0009] To solve the above technical problems, the technical solution of the present invention is as follows: The enhanced positioning method based on TDOA in complex scenes has the following specific steps:

[0010] S1: Frame the positioning data received by the base station to obtain a positioning data frame in which the positioning tag sends positioning information once and reaches multiple base stations;

[0011] S2: Determine whether the number of nodes reaching the base station meets the requirement. If so, go to step S3; if not, return to step S1.

[0012] S3: Sort the positioning data frames in ascending order according to the timestamp information size, and select at least three base stations as reference base stations based on the timestamp sorting;

[0013] S4: The reference base stations are combined in pairs and then used as benchmarks to traverse other base stations in the positioning data frame to perform positioning solutions, respectively obtaining candidate data sets under each combined benchmark. The candidate data elements in each candidate data set are then screened to obtain a hidden state data set.

[0014] S5: Combine the hidden state data set of the previous frame and the hidden state data set of the current positioning frame to calculate the state transition probability of the elements of the current positioning data set, update the transition probability of the hidden state value in the hidden state data set of the current positioning frame, filter out the state value with the largest state transition probability, and obtain the positioning result sequence;

[0015] S6: Perform weighted fusion processing on the positioning result sequence obtained in step S5 to obtain the final positioning result.

[0016] Using the above technical solution, we first use different base station combination benchmarks to solve the candidate positioning data set, covering the positioning solution set with high reliability under single positioning data. Then, we combine the historical data set to calculate the transition probability between two positioning data, screen out the most likely state transition path, and finally perform weighted fusion processing to obtain the final positioning result. There is no need for optimal topology screening and non-line-of-sight processing of the original positioning data, and at the same time it can improve the stability of positioning in complex scenarios.

[0017] Preferably, the positioning data frame in step S1 includes base station coordinate information and timestamp information of when the positioning information arrives at the base station.

[0018] Preferably, the number of base stations reached in step S2 is at least 4 to meet the requirement.

[0019] Preferably, in step S3, the three base stations at the front of the timestamp sorting are selected as reference base stations, which are respectively recorded as reference base station A, reference base station B and reference base station C; in step S4, reference base station A and reference base station B are used as a combination, reference base station A and reference base station C are used as a combination, and reference base station B and reference base station C are used as a combination as a benchmark to traverse other base stations in the positioning data frame for positioning solution, and obtain the candidate data set result_candidate under each combination benchmark respectively. AB , candidate dataset result_candidate AC and candidate dataset result_candidate BC , and then screen the candidate data elements in each candidate data set to obtain the hidden state data set.

[0020] Preferably, the specific steps of step S4 are:

[0021] S41: Using the coordinates of reference base station A and reference base station B as a combination, i.e., using the midpoint of the coordinates of reference base station A and reference base station B as the coordinate origin, rotate reference base station A and reference base station B to the X-axis, and calculate the translation parameter move_para and the rotation parameter theta;

[0022] S42: Convert all base station coordinates to a new reference coordinate system according to the translation parameter move_para and the rotation parameter theta;

[0023] S43: Based on the reference base station A and the reference base station B, the remaining base stations are traversed in turn, and the three-base station combined positioning solution is performed using the Chan algorithm to obtain the candidate data set result_candidate AB ;

[0024] S44: Repeat steps S41 to S43 using the reference base station B and reference base station C as a combination and the reference base station C and reference base station A as a combination to obtain a candidate data set result_candidate AC and candidate dataset result_candidate BC ;

[0025] S45: Use K-means clustering algorithm to screen candidate data sets result_candidate AB , candidate dataset result_candidate AC and candidate dataset result_candidate BC The candidate data elements whose Euclidean distances between the candidate data elements satisfy the set threshold;

[0026] S46: Restore the candidate data values ​​to the original coordinate system according to the translation parameter move_para and the rotation parameter theta, and finally obtain the hidden state data set. Use the clustering method to eliminate the candidate values ​​with relatively large deviations in the candidate data set, and retain the candidate values ​​with relatively high clustering degree as the hidden state data set.

[0027] Preferably, the formula for calculating the translation parameter move_para and the rotation parameter theta in step S41 is:

[0028]

[0029] Among them, (x A ,y A ) is the coordinate of the reference base station A in the original coordinate system, (x B ,yB ) is the coordinate of the reference base station B in the original coordinate system.

[0030] Preferably, in step S42, all base station coordinates are converted to a new reference coordinate system. When the reference base station is A, the calculation formula is:

[0031]

[0032] in, is the coordinate of base station A in the new reference coordinate system.

[0033] Preferably, the specific steps of calculating the state transition probability using the Viterbi algorithm in step S5 are:

[0034] Suppose the hidden state data set of the previous positioning frame is loc1_candi{j,j=1,2,...,M}, the corresponding state transition probability set is para1{j,j=1,2,...,M}, the hidden state data set of the current positioning frame is loc2_candi{i,i=1,2,...,N}, the corresponding state transition probability set is para2{i,i=1,2,...,N}, then the calculation formula of the single element para2(i) in the state transition probability set para2{i,i=1,2,...,N} is as follows:

[0035] dist i (j)=||(loc1_candi(j)-loc2_candi(i))||2;

[0036] transP i (j) = para1(j) × exp(-abs(dist i (j)) 2 );

[0037] para2(i)=max(transP i {j,j=1,2,...,M});

[0038] Among them, loc1_candi(j), loc2_candi(i) and para1(j) are the single element values ​​in loc1_candi{j,j=1,2,...,M}, loc2_candi{i,i=1,2,...,N} and para1{j,j=1,2,...,M} respectively, dist i (j) is the Euclidean distance between the i-th element in loc2_candi{i,i=1,2,...,N} and the j-th element in loc1_candi{j,j=1,2,...,M}, transP i(j) is the state transition probability of the i-th element in loc2_candi{i,i=1,2,...,N} and the j-th element in loc1_candi{j,j=1,2,...,M}.

[0039] Preferably, the calculation formula of the positioning result loc_out in step S6 is:

[0040]

[0041] Among them, loc2_candi{i,i=1,2,...,N} is the hidden state data set of this positioning frame, and the corresponding state transition probability set is para2{i,i=1,2,...,N}, N is the number of elements in the data set, and i is the index of the data set element.

[0042] Preferably, the system of the enhanced positioning method based on TDOA complex scenarios includes a positioning server, a positioning tag and multiple base stations, wherein the positioning tag is used to send UWB positioning data; the base station receives the positioning data sent by the positioning tag, records and processes the arrival timestamp information of the positioning data, and then transmits the positioning data to the positioning server; the positioning server is used to receive the positioning data transmitted by the base station, and run the enhanced positioning method based on TDOA complex scenarios to obtain the location information of the positioning tag.

[0043] Compared with the existing technology, the beneficial effects of the technical solution of the present invention are: the enhanced positioning method based on TDOA in complex scenarios adopts different base station combination benchmarks to solve the candidate positioning data set, covering the positioning solution set with high reliability under single positioning data, and then combines the historical data set to calculate the transition probability between the two positioning data, screen out the most likely state transition path, and finally perform weighted fusion processing to obtain the final positioning result, realizing the elimination of the need for optimal topology screening and non-line-of-sight processing of the original positioning data, and at the same time can improve the stability of positioning in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the enhanced positioning method based on TDOA in complex scenes of the present invention;

[0045] Figure 2 This is a flow chart of the calculation of the hidden state data set in the enhanced positioning method based on TDOA in complex scenes of the present invention;

[0046] Figure 3 Schematic diagram of coordinate system conversion in the enhanced positioning method based on TDOA in complex scenes of the present invention;

[0047] Figure 4Schematic diagram of the system structure of the enhanced positioning method based on TDOA in complex scenarios of the present invention;

[0048] Figure 5 This is a comparison chart of the positioning results of the enhanced positioning method based on TDOA in complex scenarios and the classic Chan algorithm in the same scenario. DETAILED DESCRIPTION

[0049] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration and not limitation to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0050] Example: Figure 1 As shown, the enhanced positioning method based on TDOA in complex scenes of the present invention has the following specific steps:

[0051] S1: framing the positioning data received by the base station to obtain a positioning data frame in which the positioning tag sends positioning information to multiple base stations once; the positioning data frame in step S1 includes the base station coordinate information and the timestamp information of the positioning information arriving at the base station; S2: judging whether the number of base stations arriving meets the requirement, if so, go to step S3; if not, return to step S1; if the number of base stations arriving in step S2 is at least 4, the requirement is met; the number of base stations received this time is recorded as M, if M is less than 4, the positioning is not solved this time, and the frame processing is returned to be re-framed; if M is greater than or equal to 4, a positioning data frame with M rows and 3 columns is obtained, wherein the first column represents the horizontal coordinate of the base station, the second column represents the vertical coordinate of the base station, and the third column represents the timestamp information;

[0052] S3: sorting the positioning data frames in ascending order according to the size of the timestamp information, and selecting at least three base stations as reference base stations based on the timestamp sorting. In this embodiment, in step S3, the three base stations with the highest timestamp sorting are selected as reference base stations, and are respectively recorded as reference base station A, reference base station B, and reference base station C. That is, sorting the positioning data frames in ascending order according to the size of the timestamp information, that is, adjusting the rows of the positioning data frames in ascending order from small to large according to the third column, and selecting the three base stations with the highest timestamp sorting as reference base stations, and are respectively recorded as reference base station A, reference base station B, and reference base station C.

[0053] S4: The reference base stations are combined in pairs and then used as benchmarks to traverse other base stations in the positioning data frame to perform positioning solutions, respectively obtaining candidate data sets under each combined benchmark. The candidate data elements in each candidate data set are then screened to obtain a hidden state data set.

[0054] In this embodiment, in step S4, reference base station A and reference base station B are used as a combination, reference base station A and reference base station C are used as a combination, and reference base station B and reference base station C are used as a combination as a reference to traverse other base stations in the positioning data frame to perform positioning solutions, and obtain the candidate data set result_candidate under each combination benchmark respectively. AB , candidate dataset result_candidate AC and candidate dataset result_candidate BC , then screen the candidate data elements in each candidate data set to obtain the hidden state data set; in each candidate data set, use the clustering method to eliminate the candidate values ​​with relatively large deviations in the candidate data set, and retain the candidate values ​​with relatively high clustering degree as the hidden state data set;

[0055] The specific steps of step S4 are:

[0056] S41: Using the coordinates of reference base station A and reference base station B as a combination, i.e., using the midpoint of the coordinates of reference base station A and reference base station B as the coordinate origin, rotate reference base station A and reference base station B to the X-axis, and calculate the translation parameter move_para and the rotation parameter theta;

[0057] S42: Convert all base station coordinates to a new reference coordinate system according to the translation parameter move_para and the rotation parameter theta;

[0058] S43: Based on the reference base station A and reference base station B, traverse the remaining base stations in turn, use the classic Chan algorithm to perform three-base station combined positioning solution, and obtain the candidate data set result_candidate AB ;

[0059] S44: Repeat steps S41 to S43 using the reference base station B and reference base station C as a combination and the reference base station C and reference base station A as a combination to obtain a candidate data set result_candidate AC and candidate dataset result_candidate BC ;

[0060] S45: Use K-means clustering algorithm to screen candidate data sets result_candidate AB, candidate dataset result_candidate AC and candidate dataset result_candidate BC The candidate data elements whose Euclidean distances between the candidate data elements satisfy the set threshold;

[0061] S46: restore the candidate data value to the original coordinate system according to the translation parameter move_para and the rotation parameter theta, and finally obtain the hidden state data set;

[0062] In this embodiment, Figure 2 As shown, the AB combination benchmark is used as an example for explanation;

[0063] (1) Using the midpoint of the reference base station A and the reference base station B as the coordinate origin, rotate the two base stations A and B to the X-axis, and calculate the translation parameter move_para and the rotation parameter theta;

[0064] The specific coordinate system conversion diagram is as follows Figure 3 As shown, the original coordinate system is OXY, and the coordinate system after translation and rotation is O′X′Y′. The specific calculation method of the translation parameter move_para and the rotation parameter theta is:

[0065]

[0066]

[0067] Among them, (x A ,y A ) is the coordinate of the reference base station A in the original coordinate system OXY, (x B ,y B ) are the coordinates of the reference base station B in the original coordinate system OXY;

[0068] (2) According to the translation parameter move_para and rotation parameter theta calculated in step (1), all base station coordinates are converted to the new reference coordinate system O′X′Y′;

[0069] The specific calculation formula is explained using base station A as an example:

[0070]

[0071] in, is the coordinate of base station A in the new reference coordinate system O′X′Y′;

[0072] (3) Based on the reference base station A and the reference base station B, traverse the remaining base stations one by one, and use the classic Chan algorithm to perform three-base station combined positioning solution to obtain the candidate data set result_candidate AB ;

[0073] In this embodiment, the number of received base stations M = 6 is used as an example. Under the premise of reference base station A and reference base station B as the benchmark, each time a remaining reference base station is traversed, M-2, that is, 4 groups of base station combinations can be obtained, and the candidate data set result_candidate AB The number of elements in is 4;

[0074] (4) Use K-means clustering algorithm to screen candidate data sets result_candidate AB The candidate data elements whose Euclidean distance between them meets the set threshold; the threshold of the Euclidean distance can be dynamically adjusted according to different positioning targets. In this embodiment, taking personnel positioning as an example, the default threshold is 2 meters;

[0075] (5) According to the translation parameter move_para and the rotation parameter theta, the candidate data value is restored to the original coordinate system OXY to obtain the final hidden state data set loc_candi AB ;

[0076] Since the coordinates of the base station are transformed before positioning solution, the candidate dataset result_candidate AB The solutions are all in the new coordinate system O′X′Y′, and the candidate solutions need to be restored to the original coordinate system OXY;

[0077] According to the processing method of the reference base station A and reference base station B combination benchmark, the reference base station A and reference base station C combination and the reference base station B and reference base station C combination are processed in the same way to obtain the hidden state data set loc_candi AC And hidden state dataset loc_candi BC , and then merge these three data sets into a hidden state data set loc_candi;

[0078] S5: Combining the hidden state data set of the previous frame and the hidden state data set of the current positioning frame, using the Viterbi algorithm to calculate the state transition probability, combining the transition probability of the hidden state value in the hidden state data set of the previous frame, updating the transition probability of the hidden state value in the hidden state data set of the current positioning frame, screening out the state value with the largest transition probability, and obtaining a positioning result sequence; the specific method for calculating the state transition probability in step S5 is:

[0079] Suppose the hidden state data set of the previous positioning frame is loc1_candi{j,j=1,2,...,M}, the corresponding state transition probability set is para1{j,j=1,2,...,M}, the hidden state data set of the current positioning frame is loc2_candi{i,i=1,2,...,N}, the corresponding state transition probability set is para2{i,i=1,2,...,N}, then the calculation formula of the single element para2(i) in the state transition probability set para2{i,i=1,2,...,N} is as follows:

[0080] dist i (j)=||(loc1_candi(j)-loc2_candi(i))||2;

[0081] transP i (j) = para1(j) × exp(-abs(dist i (j)) 2 );

[0082] para2(i)=max(transP i {j,j=1,2,...,M});

[0083] Among them, loc1_candi(j), loc2_candi(i) and para1(j) are the single element values ​​in loc1_candi{j,j=1,2,...,M}, loc2_candi{i,i=1,2,...,N} and para1{j,j=1,2,...,M} respectively, dist i (j) is the Euclidean distance between the i-th element in loc2_candi{i,i=1,2,...,N} and the j-th element in loc1_candi{j,j=1,2,...,M}, transP i (j) is the state transition probability of the i-th element in loc2_candi{i,i=1,2,...,N} and the j-th element in loc1_candi{j,j=1,2,...,M};

[0084] S6: Perform weighted fusion processing on the positioning result sequence obtained in step S5 to obtain the final positioning result; the specific calculation formula of the positioning result loc_out is:

[0085]

[0086] Among them, loc2_candi{i,i=1,2,...,N} is the hidden state data set of this positioning frame, and the corresponding state transition probability set is para2{i,i=1,2,...,N}, N is the number of elements in the data set, and i is the index of the data set element.

[0087] like Figure 4 Figure 2 shows a schematic diagram of a UWB TDOA positioning system provided by an embodiment of the present invention. The system includes at least one positioning tag, multiple base stations, and a positioning server. The positioning tag is used to send UWB positioning data; the base station receives the positioning data sent by the positioning tag, records and processes the arrival timestamp information of the positioning data, and then transmits the positioning data to the positioning server. The positioning server is used to receive the positioning data transmitted by the base station and run the enhanced positioning method based on TDOA complex scenarios to obtain the location information of the positioning tag.

[0088] In order to verify the enhanced positioning method based on TDOA in complex scenarios described in this embodiment, this embodiment conducted a comparative experiment based on data collected from a specific project. Figure 5 As shown, 8 base stations are deployed at the project site, and personnel carry positioning tags and continuously walk in the positioning area to collect original positioning data. The enhanced positioning method described in this embodiment and the classic Chan algorithm with base station screening are used to perform positioning calculations on the data collected this time, and the corresponding positioning trajectory is obtained under each method. Figure 5 It can be seen that in such a complex engineering site, the CHAN algorithm may produce jumps of several meters or even more than ten meters in some places, and its stability is insufficient. The enhanced positioning method described in this embodiment has more stable performance and a smoother trajectory.

[0089] In the above embodiments, the description of each embodiment has different emphases. Parts that are not described or recorded in detail in a certain embodiment can be included in the relevant description of other embodiments.

[0090] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

Claims

1. An enhanced positioning method based on TDOA in complex scenes, characterized by: The specific steps are: S1: Frame the positioning data received by the base station to obtain a positioning data frame in which the positioning tag sends positioning information once and reaches multiple base stations; S2: Determine whether the number of nodes reaching the base station meets the requirement. If so, go to step S3; if not, return to step S1. S3: Sort the positioning data frames in ascending order according to the timestamp information size, and select at least three base stations as reference base stations based on the timestamp sorting; S4: The reference base stations are combined in pairs and then used as benchmarks to traverse other base stations in the positioning data frame to perform positioning solutions, respectively obtaining candidate data sets under each combined benchmark. The candidate data elements in each candidate data set are then screened to obtain a hidden state data set. S5: Combine the hidden state data set of the previous frame and the hidden state data set of the current positioning frame to calculate the state transition probability of the elements of the current positioning data set, update the transition probability of the hidden state value in the hidden state data set of the current positioning frame, filter out the state value with the largest state transition probability, and obtain the positioning result sequence; S6: performing weighted fusion processing on the positioning result sequence obtained in step S5 to obtain the final positioning result; The positioning data frame in step S1 includes base station coordinate information and timestamp information of the positioning information arriving at the base station; the system based on the enhanced positioning method in complex TDOA scenarios includes a positioning server, a positioning tag and multiple base stations.

2. The enhanced positioning method based on TDOA in complex scenes according to claim 1, characterized in that: In step S3, the three base stations with the highest timestamps are selected as reference base stations, which are respectively denoted as reference base station A, reference base station B, and reference base station C; In step S4, reference base station A and reference base station B are used as a combination, reference base station A and reference base station C are used as a combination, and reference base station B and reference base station C are used as a combination as a reference to traverse other base stations in the positioning data frame for positioning solution, and the candidate data set result_candidate under each combination benchmark is obtained respectively. AB , candidate dataset result_candidate AC and candidate dataset result_candidate BC , and then screen the candidate data elements in each candidate data set to obtain the hidden state data set.

3. The enhanced positioning method based on TDOA in complex scenes according to claim 2, characterized in that: The specific steps of step S4 are: S41: Using the coordinates of reference base station A and reference base station B as a combination, i.e., using the midpoint of the coordinates of reference base station A and reference base station B as the coordinate origin, rotate reference base station A and reference base station B to the X-axis, and calculate the translation parameter move_para and the rotation parameter theta; S42: Convert all base station coordinates to a new reference coordinate system according to the translation parameter move_para and the rotation parameter theta; S43: Based on the reference base station A and the reference base station B, the remaining base stations are traversed in turn, and the three-base station combined positioning solution is performed using the Chan algorithm to obtain the candidate data set result_candidate AB ; S44: Repeat steps S41 to S43 using the reference base station B and reference base station C as a combination and the reference base station C and reference base station A as a combination to obtain a candidate data set result_candidate AC and candidate dataset result_candidate BC ; S45: Use K-means clustering algorithm to screen candidate data sets result_candidate AB , candidate dataset result_candidate AC and candidate dataset result_candidate BC The candidate data elements whose Euclidean distances between the candidate data elements satisfy the set threshold; S46: Restore the candidate data values ​​to the original coordinate system according to the translation parameter move_para and the rotation parameter theta, and finally obtain the hidden state data set.

4. The enhanced positioning method based on TDOA in complex scenes according to claim 3 is characterized in that The formula for calculating the translation parameter move_para and the rotation parameter theta in step S41 is: Among them, (x A ,y A ) is the coordinate of the reference base station A in the original coordinate system, (x B ,y B ) is the coordinate of the reference base station B in the original coordinate system.

5. The enhanced positioning method based on TDOA in complex scenes according to claim 4, characterized in that: In step S42, all base station coordinates are converted to the new reference coordinate system. When the reference base station is A, the calculation formula is: in, is the coordinate of base station A in the new reference coordinate system.

6. The enhanced positioning method based on TDOA in complex scenes according to claim 5, characterized in that: The specific steps of calculating the state transition probability using the Viterbi algorithm in step S5 are: Suppose the hidden state data set of the previous positioning frame is loc1_candi{j,j=1,2,...,M}, the corresponding state transition probability set is para1{j,j=1,2,...,M}, the hidden state data set of the current positioning frame is loc2_candi{i,i=1,2,...,N}, the corresponding state transition probability set is para2{i,i=1,2,...,N}, then the calculation formula of the single element para2(i) in the state transition probability set para2{i,i=1,2,...,N} is as follows: distance i (j)=||(loc1_candi(j)-loc2_candi(i))||2; transP i (j)=para1(j)×exp(-abs(dist i (j)) 2 ); para2(i)=max(transP i {j,j=1,2,...,M}); Among them, loc1_candi(j), loc2_candi(i) and para1(j) are the single element values ​​in loc1_candi{j,j=1,2,...,M}, loc2_candi{i,i=1,2,...,N} and para1{j,j=1,2,...,M} respectively, dist i (j) is the Euclidean distance between the i-th element in loc2_candi{i,i=1,2,...,N} and the j-th element in loc1_candi{j,j=1,2,...,M}, transP i (j) is the state transition probability of the i-th element in loc2_candi{i,i=1,2,...,N} and the j-th element in loc1_candi{j,j=1,2,...,M}.

7. The enhanced positioning method based on TDOA in complex scenes according to claim 3, characterized in that: The calculation formula of the positioning result loc_out in step S6 is: Among them, loc2_candi{i,i=1,2,...,N} is the hidden state data set of this positioning frame, and the corresponding state transition probability set is para2{i,i=1,2,...,N}, N is the number of elements in the data set, and i is the index of the data set element.

8. The enhanced positioning method based on TDOA in complex scenes according to any one of claims 1 to 7, characterized in that: The positioning tag is used to send UWB positioning data; the base station receives the positioning data sent by the positioning tag, records and processes the arrival timestamp information of the positioning data, and then transmits the positioning data to the positioning server; the positioning server is used to receive the positioning data transmitted by the base station and run the enhanced positioning method based on TDOA complex scenarios to obtain the location information of the positioning tag.

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