CN116390021A - Enhanced positioning method based on TDOA (time difference of arrival) complex scene - Google Patents

Enhanced positioning method based on TDOA (time difference of arrival) complex scene Download PDF

Info

Publication number
CN116390021A
CN116390021A CN202310287564.7A CN202310287564A CN116390021A CN 116390021 A CN116390021 A CN 116390021A CN 202310287564 A CN202310287564 A CN 202310287564A CN 116390021 A CN116390021 A CN 116390021A
Authority
CN
China
Prior art keywords
positioning
base station
data set
candidate
reference base
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310287564.7A
Other languages
Chinese (zh)
Inventor
马琳琳
肖岩
李冀
杨新鑫
宋六环
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Henan Lianrui Intelligent Technology Research Institute Co ltd
Zhengzhou Locaris Electronic Technology Co ltd
Original Assignee
Henan Lianrui Intelligent Technology Research Institute Co ltd
Zhengzhou Locaris Electronic Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Henan Lianrui Intelligent Technology Research Institute Co ltd, Zhengzhou Locaris Electronic Technology Co ltd filed Critical Henan Lianrui Intelligent Technology Research Institute Co ltd
Priority to CN202310287564.7A priority Critical patent/CN116390021A/en
Publication of CN116390021A publication Critical patent/CN116390021A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses an enhanced positioning method based on a TDOA complex scene, which comprises the following steps: s1, framing according to positioning data received by a base station, and obtaining positioning data frames of positioning tags which send positioning information once to reach a plurality of base stations; s2, judging whether the number of the reached base stations meets the requirement; s3, ordering the positioning data frames according to the size of the time stamp information, and selecting a reference base station; s4, respectively combining the reference base stations in pairs, sequentially using the reference base stations as the reference to traverse other base stations in the positioning data frame for positioning and resolving to respectively obtain candidate data sets under each combined reference, and screening candidate data elements in each candidate data set to obtain a hidden state data set; s5, combining the hidden state data sets of the previous frame and the current positioning frame, calculating the state transition probability of the current positioning data set element, and screening the state transition probability to obtain a positioning result sequence; s6, obtaining a final positioning result. The method combines with the historical position to improve the positioning stability.

Description

Enhanced positioning method based on TDOA (time difference of arrival) complex scene
Technical Field
The invention belongs to the technical field of wireless positioning, and particularly relates to an enhanced positioning method based on a TDOA (time difference of arrival) complex scene.
Background
Besides, the UWB technology has the advantages of low system complexity, high information security, strong multipath fading resistance and the like, and becomes a big bright point in the field of wireless positioning.
The UWB positioning accuracy based on TDOA is mainly affected by wireless signal propagation and base station topology. Problems such as signal reflection, multipath propagation, non-Line-Of-Sight (NLOS) and the like all generate different measurement errors, thereby affecting the stability and accuracy Of the positioning result. In addition, the number of the base stations and the layout topology structure can influence the resolving precision of the TDOA positioning algorithm, and the good topology structure means that the base stations are not concentrated in one area in spatial distribution and can be uniformly distributed in different azimuth areas, so that certain requirements are provided for the number and the arrangement mode of the base stations.
The main means for improving the positioning performance of TDOA in complex scenes at present include: non-line-of-sight identification is carried out on the original positioning data to remove NLOS data, topology screening is carried out on the received base station data to ensure the stability of positioning, the complexity of an algorithm and the instantaneity of back-end calculation are certainly increased by the data processing, and meanwhile, the quantity of the positioning data is reduced, so that the smoothness of a positioning track is affected.
Chinese patent document (CN 109041207 a) discloses a precise positioning system based on BIM technology that can be used for virtual reality and augmented reality, comprising: a UWB signal transmission module; a plurality of UWB signal receiving base stations; the data processing module is used for determining the positions of the UWB signal sending module relative to the N UWB signal receiving base stations according to the TDOA algorithm; a data storage module; the cameras are uniformly arranged in the construction tunnel; the video monitoring module is used for receiving video information shot by each camera; the BIM is used for presetting an electronic map and the like in the construction tunnel, and converting the position information of the stored UWB signal sending module relative to the N UWB signal receiving base stations into position coordinates on the electronic map in the construction tunnel; virtual reality or augmented reality display devices. But the positioning system can not solve the difficulty of wireless signal non-line-of-sight identification and base station screening.
Chinese patent document (CN 113030859 a) discloses a UWB indoor positioning method based on time division multiple access, comprising the steps of: setting N base stations BSi in a positioning area; the terminal MS transmits a UWB signal once and records a transmission time stamp TMST; after receiving the UWB signal, the base station BSi records a receiving time stamp TBSR1 (i); the central base station transmits a UWB signal once and records a transmission time stamp TBST; recording a receiving time stamp TBSR2 (i) after the positioning base station receives the UWB signal; and constructing a positioning resolving matrix by the parameters to obtain the coordinates of the terminal MS. According to the UWB indoor positioning method, N base stations BSi are utilized for combined positioning, so that the positioning precision of a terminal MS can be effectively enhanced, and the load balance of the base stations is realized; and the terminal MS is positioned by using the two-time UWB signal emission and through a TDOA positioning method, so that the deployment cost of the UWB indoor positioning system is reduced. However, the positioning method still has unstable positioning.
Disclosure of Invention
Aiming at the problems, the invention provides an enhanced positioning method based on a TDOA complex scene, which can improve the positioning performance of UWB in the complex scene, solve the difficulties of non-line-of-sight identification and base station screening of wireless signals, and simultaneously combine historical position information to improve the positioning stability.
In order to solve the technical problems, the technical scheme of the invention is as follows: the enhanced positioning method based on the TDOA complex scene comprises the following specific steps:
s1: framing is carried out according to positioning data received by the base stations, and positioning data frames of positioning tags which send positioning information once to reach a plurality of base stations are obtained;
s2: judging whether the number of the reached base stations meets the requirement, if so, turning to step S3; if not, returning to the step S1;
s3: ascending sort is carried out on the positioning data frames according to the size of the time stamp information, and at least 3 base stations are selected as reference base stations according to the time stamp sort;
s4: respectively combining the reference base stations in pairs, sequentially using the combined reference base stations as the reference to traverse other base stations in the positioning data frame for positioning calculation, respectively obtaining candidate data sets under each combined reference, and screening candidate data elements in each candidate data set to obtain a hidden state data set;
s5: combining the hidden state data set of the previous frame and the hidden state data set of the current positioning frame, calculating the state transition probability of the current positioning data set element, updating the transition probability of the hidden state value in the hidden state data set of the current positioning frame, and screening out the state value with the maximum state transition probability to obtain a positioning result sequence;
s6: and (5) carrying out weighted fusion processing on the positioning result sequence obtained in the step (S5) to obtain a final positioning result.
According to the technical scheme, firstly, the candidate positioning data sets are calculated by adopting different base station combination references, the positioning solution set with high reliability under single positioning data is covered, then, the transition probability calculation between the two positioning data is calculated by combining the historical data sets, the most likely state transition path is screened out, finally, the final positioning result is obtained by carrying out weighted fusion processing, the optimal topology screening and the non-line-of-sight processing of the original positioning data are not needed, and meanwhile, the positioning stability under a complex scene can be improved.
Preferably, the positioning data frame in step S1 includes base station coordinate information and time stamp information of arrival of the positioning information at the base station.
Preferably, the number of reaching base stations in step S2 is at least 4.
Preferably, in the step S3, the first 3 base stations with time stamps ordered are selected as reference base stations, and are respectively marked as reference base station a, reference base station B and reference base station C; in the step S4, reference base station a and reference base station B are combined in sequence, respectively, and referenceThe base station A and the reference base station C are combined, the reference base station B and the reference base station C are combined to serve as the base stations to traverse other base stations in the positioning data frame for positioning calculation, and a candidate data set result_candidate under each combined base is obtained respectively AB Candidate data set result_candidate AC And candidate data set result_candidate BC And screening the candidate data elements in each candidate data set to obtain the hidden state data set.
Preferably, the specific steps of the step S4 are as follows:
s41: taking the coordinates of the reference base station A and the reference base station B as a combination, namely taking the middle point of the coordinates of the reference base station A and the coordinates of the reference base station B as a coordinate origin, rotating the two base stations of the reference base station B of the reference base station A onto an X axis, and calculating to obtain a translation parameter move_para and a rotation parameter theta;
s42: converting all base station coordinates into a new reference coordinate system according to the translation parameter move_para and the rotation parameter theta;
s43: on the basis of the reference base station A and the reference base station B, traversing the rest base stations in sequence, and carrying out three-base station combined positioning calculation by using a chan algorithm to obtain a candidate data set result_candidate AB ；
S44: repeating steps S41-S43 with reference base station B and reference base station C as combination and reference base station C and reference base station A as combination respectively to obtain candidate data set result_candidate AC And candidate data set result_candidate BC ；
S45: screening candidate data set result_candidate by using K-means clustering algorithm AB Candidate data set result_candidate AC And candidate data set result_candidate BC The Euclidean distance between candidate data elements meets the set threshold value;
s46: and restoring the candidate data value to the original coordinate system according to the translation parameter move_para and the rotation parameter theta, and finally obtaining the hidden state data set. And removing the candidate value with larger deviation in the candidate data set by using a clustering method, and reserving the candidate value with higher clustering degree as the hidden state data set.
Preferably, the formula for calculating the translation parameter move_para and the rotation parameter theta in the step S41 is as follows:
Figure SMS_1
Figure SMS_2
wherein, (x) A ,y A ) For reference base station A coordinates in the original coordinate system, (x B ,y B ) Is the coordinates of reference base station B in the original coordinate system.
Preferably, in the step S42, all base station coordinates are converted into a new reference coordinate system, and when the reference base station is a, the calculation formula is:
Figure SMS_3
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_4
is the coordinates of base station a in the new reference coordinate system.
Preferably, the specific step of calculating the state transition probability in the step S5 by using the viterbi algorithm is as follows:
let the hidden state data set of the previous positioning frame be loc1_candi { j, j=1, 2,., M }, the corresponding state transition probability set be para1{ j, j=1, 2,., M }, the hidden state data set of the current positioning frame be loc2_candi { i, i=1, 2,., N }, the corresponding state transition probability set be para2{ i, i=1, 2,.,. N }, the calculation formula of the single element para2 (i) in the state transition probability set para2{ i, i=1, 2,. N } is as follows:
dist 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})；
wherein loc1_candi (j), loc2_candi (i), and para1 (j) are loc1_candi { j, j=1, 2,., (j), M }, loc2_candi { i, i=1, 2, N } and para1{ j, j=1, 2, single element value in M }, dist i (j) The euclidean distance, trans p, for 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 } i (j) The state transition probabilities for the i-th element in loc2_candi { i, i=1, 2,...
Preferably, the calculation formula of the positioning result loc_out in the step S6 is:
Figure SMS_5
wherein loc2_candi { i, i=1, 2, & gt, N } is a hidden state data set of the positioning frame, a state transition probability set corresponding to the hidden state data set is para2{ i, i=1, 2, & gt, N } and N is the number of elements in the data set, and i is an index of the elements in the data set.
Preferably, the system for enhancing the positioning method in the TDOA-based complex scene comprises a positioning server, a positioning tag and a plurality of base stations, wherein the positioning tag is used for transmitting UWB positioning data; the base station receives the positioning data sent by the positioning tag, records and processes the arrival time stamp information of the positioning data, and then transmits the positioning data to the positioning server; the positioning server is used for receiving the positioning data transmitted by the base station and running the enhanced positioning method under the TDOA-based complex scene to obtain the position information of the positioning tag.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: according to the enhanced positioning method based on the TDOA complex scene, different base station combination references are adopted to conduct candidate positioning data set resolving, a positioning solution set with high reliability under single positioning data is covered, then the historical data set is combined to calculate transition probability calculation between two positioning data, the most likely state transition path is screened out, finally weighting fusion processing is conducted to obtain a final positioning result, optimal topology screening and non-line-of-sight processing of original positioning data are not needed, and meanwhile positioning stability under the complex scene can be improved.
Drawings
FIG. 1 is a flow chart of an enhanced positioning method in a TDOA-based complex scene of the present invention;
FIG. 2 is a flow chart of the calculation of a hidden state dataset in an enhanced localization method based on a TDOA complex scene of the present invention;
FIG. 3 is a schematic diagram of coordinate system conversion in the enhanced localization method under a TDOA-based complex scene of the present invention;
FIG. 4 is a schematic diagram of a system architecture of an enhanced positioning method in a TDOA-based complex scenario of the present invention;
fig. 5 is a comparison diagram of positioning results of the enhanced positioning method based on the TDOA complex scene and the classical chan algorithm in the same scene.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, 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.
Examples: as shown in fig. 1, the enhanced positioning method based on the TDOA complex scene of the present invention specifically includes the following steps:
s1: framing is carried out according to positioning data received by the base stations, and positioning data frames of positioning tags which send positioning information once to reach a plurality of base stations are obtained; the positioning data frame in the step S1 comprises base station coordinate information and time stamp information of the positioning information reaching the base station; s2: judging whether the number of the reached base stations meets the requirement, if so, turning to step S3; if not, returning to the step S1; the number of the reached base stations in the step S2 is at least 4, and the requirement is met; recording the number of the received base stations as M, if M is smaller than 4, the positioning is not resolved at this time, and returning to framing again; if M is greater than or equal to 4, obtaining a positioning data frame of M rows and 3 columns, wherein the first column represents the abscissa of the base station, the second column represents the ordinate of the base station, and the third column represents the timestamp information;
s3: ascending sort is carried out on the positioning data frames according to the size of the time stamp information, and at least 3 base stations are selected as reference base stations according to the time stamp sort; in this embodiment, in the step S3, the first 3 base stations with time stamps ordered are selected as reference base stations, and are respectively marked as reference base station a, reference base station B, and reference base station C; the method comprises the steps that ascending order sorting is conducted on positioning data frames according to the size of timestamp information, ascending order adjustment is conducted on the positioning data frames according to the third column from small to large, 3 base stations with the forefront timestamp sorting are selected to serve as reference base stations, and the reference base stations A, the reference base station B and the reference base station C are marked respectively;
s4: respectively combining the reference base stations in pairs, sequentially using the combined reference base stations as the reference to traverse other base stations in the positioning data frame for positioning calculation, respectively obtaining candidate data sets under each combined reference, and screening candidate data elements in each candidate data set to obtain a hidden state data set;
in this embodiment, in step S4, reference base station a and reference base station B are sequentially used as a combination, reference base station a and reference base station C are used as a combination, reference base station B and reference base station C are used as a combination to traverse other base stations in the positioning data frame as the reference, and positioning solution is performed to obtain candidate data sets result_candidate under each combination reference respectively AB Candidate data set result_candidate AC And candidate data set result_candidate BC Screening the candidate data elements in each candidate data set to obtain a hidden state data set; removing candidate values with larger deviation in the candidate data sets by using a clustering method in each candidate data set, and reserving the candidate values with higher clustering degree as hidden state data sets;
the specific steps of the step S4 are as follows:
s41: taking the coordinates of the reference base station A and the reference base station B as a combination, namely taking the middle point of the coordinates of the reference base station A and the coordinates of the reference base station B as a coordinate origin, rotating the two base stations of the reference base station B of the reference base station A onto an X axis, and calculating to obtain a translation parameter move_para and a rotation parameter theta;
s42: converting all base station coordinates into a new reference coordinate system according to the translation parameter move_para and the rotation parameter theta;
s43: on the basis of the reference base station A and the reference base station B, traversing the rest base stations in sequence, and carrying out three-base station combined positioning calculation by using a classical chan algorithm to obtain a candidate data set result_candidate AB ；
S44: repeating steps S41-S43 with reference base station B and reference base station C as combination and reference base station C and reference base station A as combination respectively to obtain candidate data set result_candidate AC And candidate data set result_candidate BC ；
S45: screening candidate data set result_candidate by using K-means clustering algorithm AB Candidate data set result_candidate AC And candidate data set result_candidate BC The Euclidean distance between candidate data elements meets the set threshold value;
s46: restoring the candidate data value to the original coordinate system according to the translation parameter move_para and the rotation parameter theta to finally obtain a hidden state data set;
in this embodiment, as shown in fig. 2, an AB combination standard is taken as an example for explanation;
(1) Taking the middle points of the coordinates of the reference base station A and the coordinates of the reference base station B as the origin of coordinates, rotating the two base stations of the reference base station A and the reference base station B onto an X axis, and calculating to obtain a translation parameter move_para and a rotation parameter theta;
the specific coordinate system conversion schematic diagram is shown in fig. 3, the original coordinate system is OXY, the coordinate system after translation and rotation is O ' X ' Y ', and the specific calculation method of the translation parameter move_para and the rotation parameter theta is as follows:
Figure SMS_6
Figure SMS_7
wherein, (x) A ,y A ) For reference base station A coordinates in the original coordinate system OXY, (x) B ,y B ) The coordinates of the reference base station B under the original coordinate system OXY;
(2) Converting all base station coordinates into a new reference coordinate system O ' X ' Y ' according to the translation parameter move_para and the rotation parameter theta calculated in the step (1);
the specific calculation formula is described by taking a reference base station A as an example:
Figure SMS_8
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_9
coordinates of the base station A under a new reference coordinate system O ' X ' Y ';
(3) Traversing one residual base station in turn on the basis of the reference base station A and the reference base station B, and carrying out three-base station combined positioning calculation by using a classical chan algorithm to obtain a candidate data set result_candidate AB ；
In this embodiment, taking the number of received base stations m=6 as an example, on the premise of taking reference base station a and reference base station B as references, traversing one remaining reference base station at a time can obtain M-2, i.e. 4 groups of base station combinations, and the obtained candidate data set result_candidate AB The number of the elements in the method is 4;
(4) Screening candidate data set result_candidate by using K-means clustering algorithm AB The Euclidean distance between candidate data elements meets the set threshold value; according to different positioning targets, the threshold value of the Euclidean distance can be dynamically adjusted, and in the embodiment, the personnel positioning is taken as an example, and the default threshold value is 2 meters;
(5) Restoring the candidate data value to the original coordinate system OXY according to the translation parameter move_para and the rotation parameter theta to obtain the final productIs a hidden state data set loc_candi AB ；
Since the coordinates of the base station are converted before the positioning solution is performed, the candidate data set result_candidate AB The solutions in (a) are under a new coordinate system O ' X ' Y ', and the candidate solutions need to be restored to an original coordinate system OXY;
according to the processing mode of the reference base station A and the reference base station B combined datum, the reference base station A and the reference base station C combined and the reference base station B and the reference base station C combined are processed in the same way to obtain a hidden state data set loc_candi AC And a hidden state data set loc_candi BC Then combining the three data sets into a hidden state data set loc_candi;
s5: calculating state transition probability by using a Viterbi algorithm by combining the hidden state data set of the previous frame and the hidden state data set of the current positioning frame, updating the transition probability of the hidden state value in the hidden state data set of the current positioning frame by combining the transition probability of the hidden state value in the hidden state data set of the previous frame, and screening out the state value with the maximum transition probability to obtain a positioning result sequence; the specific method for calculating the state transition probability in step S5 is as follows:
let the hidden state data set of the previous positioning frame be loc1_candi { j, j=1, 2,., M }, the corresponding state transition probability set be para1{ j, j=1, 2,., M }, the hidden state data set of the current positioning frame be loc2_candi { i, i=1, 2,., N }, the corresponding state transition probability set be para2{ i, i=1, 2,.,. N }, the calculation formula of the single element para2 (i) in the state transition probability set para2{ i, i=1, 2,. N } is as follows:
dist 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})；
wherein loc1_candi (j), loc2_candi (i), and para1 (j) are loc1_candi { j, j=1, 2,., (j), M }, loc2_candi { i, i=1, 2, N } and para1{ j, j=1, 2, single element value in M }, dist i (j) Is loc2_candi { i, i =1, 2..the i-th element in N } and loc1_candi { j, j=1, 2,..the euclidean distance, trans p, of the j-th element in M } i (j) The state transition probabilities for 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 };
s6: performing weighted fusion processing on the positioning result sequence obtained in the step S5 to obtain a final positioning result; the specific calculation formula of the positioning result loc_out is as follows:
Figure SMS_10
wherein loc2_candi { i, i=1, 2, & gt, N } is a hidden state data set of the positioning frame, a state transition probability set corresponding to the hidden state data set is para2{ i, i=1, 2, & gt, N } and N is the number of elements in the data set, and i is an index of the elements in the data set.
As shown in fig. 4, a schematic diagram of a UWB TDOA location system according to an embodiment of the present invention includes at least one location tag, a plurality of base stations, and a location server. The positioning tag is used for sending UWB positioning data; the base station receives the positioning data sent by the positioning tag, records and processes the arrival time stamp information of the positioning data, and then transmits the positioning data to the positioning server; the positioning server is used for receiving positioning data transmitted by the base station, and running the enhanced positioning and enhanced positioning method based on the TDOA complex scene to obtain the position information of the positioning tag.
In order to verify the enhanced positioning method based on the TDOA complex scene in the embodiment, a comparison experiment is performed in the embodiment based on the data collected in the specific project. As shown in fig. 5, 8 base stations are deployed on the site of the engineering project, personnel carry positioning labels to continuously walk in a positioning area, original positioning data are collected, and positioning and resolving are respectively carried out on the collected data by adopting the enhanced positioning method and the classical chan algorithm with base station screening according to the embodiment, so as to obtain a corresponding positioning track under each method. As can be seen from FIG. 5, in the complex engineering field, the chan algorithm can generate jitter of several meters or even more than ten meters in some differences, and the stability is insufficient, and the performance of the enhanced positioning method is more stable and the track is smoother.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and the portions of one embodiment that are not described or illustrated in detail may be related to other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Claims (9)

1. The enhanced positioning method based on the TDOA complex scene is characterized by comprising the following specific steps:
s1: framing is carried out according to positioning data received by the base stations, and positioning data frames of positioning tags which send positioning information once to reach a plurality of base stations are obtained;
s2: judging whether the number of the reached base stations meets the requirement, if so, turning to step S3; if not, returning to the step S1;
s3: ascending sort is carried out on the positioning data frames according to the size of the time stamp information, and at least 3 base stations are selected as reference base stations according to the time stamp sort;
s4: respectively combining the reference base stations in pairs, sequentially using the combined reference base stations as the reference to traverse other base stations in the positioning data frame for positioning calculation, respectively obtaining candidate data sets under each combined reference, and screening candidate data elements in each candidate data set to obtain a hidden state data set;
s5: combining the hidden state data set of the previous frame and the hidden state data set of the current positioning frame, calculating the state transition probability of the current positioning data set element, updating the transition probability of the hidden state value in the hidden state data set of the current positioning frame, and screening out the state value with the maximum state transition probability to obtain a positioning result sequence;
s6: and (5) carrying out weighted fusion processing on the positioning result sequence obtained in the step (S5) to obtain a final positioning result.
2. The enhanced positioning method based on TDOA complex scene of claim 1, wherein the positioning data frame in step S1 includes base station coordinate information and a time stamp information of arrival of the positioning information at the base station; the number of the arriving base stations in the step S2 is at least 4, which meets the requirement.
3. The enhanced positioning method based on TDOA complex scene as defined in claim 1, wherein 3 base stations with the first 3 base stations with time stamps ordered are selected as reference base stations in step S3, and are respectively denoted as reference base station a, reference base station B and reference base station C; in the step S4, the reference base station a and the reference base station B are sequentially used as a combination, the reference base station a and the reference base station C are used as a combination, and the reference base station B and the reference base station C are used as a combination to traverse other base stations in the positioning data frame as the reference to perform positioning calculation, so as to obtain candidate data sets result_candidates under each combination reference respectively AB Candidate data set result_candidate AC And candidate data set result_candidate BC And screening the candidate data elements in each candidate data set to obtain the hidden state data set.
4. The enhanced positioning method based on TDOA complex scene of claim 3, wherein the specific steps of step S4 are:
s41: taking the coordinates of the reference base station A and the reference base station B as a combination, namely taking the middle point of the coordinates of the reference base station A and the coordinates of the reference base station B as a coordinate origin, rotating the two base stations of the reference base station B of the reference base station A onto an X axis, and calculating to obtain a translation parameter move_para and a rotation parameter theta;
s42: converting all base station coordinates into a new reference coordinate system according to the translation parameter move_para and the rotation parameter theta;
s43: on the basis of the reference base station A and the reference base station B, traversing the rest base stations in sequence, and carrying out three-base station combined positioning calculation by using a chan algorithm to obtain a candidate data set result_candidate AB ；
S44: repeating steps S41-S43 with reference base station B and reference base station C as combination and reference base station C and reference base station A as combination respectively to obtain candidate data set result_candidate AC And candidate data set result_candidate BC ；
S45: screening candidate data set result_candidate by using K-means clustering algorithm AB Candidate data set result_candidate AC And candidate data set result_candidate BC The Euclidean distance between candidate data elements meets the set threshold value;
s46: and restoring the candidate data value to the original coordinate system according to the translation parameter move_para and the rotation parameter theta, and finally obtaining the hidden state data set.
5. The enhanced positioning method according to claim 4, wherein the formula for calculating the translation parameter move_para and the rotation parameter theta in step S41 is as follows:
Figure FDA0004140307220000021
Figure FDA0004140307220000022
wherein, (x) A ,y A ) For reference base station A coordinates in the original coordinate system, (x B ,y B ) Is the coordinates of reference base station B in the original coordinate system.
6. The enhanced positioning method according to claim 5, wherein in the step S42, all base station coordinates are converted into a new reference coordinate system, and when the reference base station is a, the calculation formula is:
Figure FDA0004140307220000023
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure FDA0004140307220000031
is the coordinates of base station a in the new reference coordinate system.
7. The enhanced positioning method based on TDOA complex scene of claim 6, wherein the specific step of calculating the state transition probability using viterbi algorithm in step S5 is as follows:
let the hidden state data set of the previous positioning frame be loc1_candi { j, j=1, 2,., M }, the corresponding state transition probability set be par1a {, j=j1, 2,., the hidden state data set of the current positioning frame be loc2_candi { i, i=1, 2,., N }, the corresponding state transition probability set be para2{ i, i=1, 2,., N }, the calculation formula of the single element para2 (i) in the state transition probability set para2{ i, i=1, 2,., N } is as follows:
dist 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})；
wherein loc1_candi (j), loc2_candi (i), and para1 (j) are loc1_candi { j, j=1, 2,., (j), M }, loc2_candi { i, i=1, 2, N } and para1{ j, j=1, 2, M } are single element values, dis i t (j is the euclidean distance 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 }, trans p i (j) The state transition probabilities for the i-th element in loc2_candi { i, i=1, 2,...
8. The enhanced positioning method based on TDOA complex scene as defined in claim 4, wherein the calculation formula of the positioning result locout in step S6 is:
Figure FDA0004140307220000032
wherein loc2_candi { i, i=1, 2, & gt, N } is a hidden state data set of the positioning frame, a state transition probability set corresponding to the hidden state data set is para2{ i, i=1, 2, & gt, N } and N is the number of elements in the data set, and i is an index of the elements in the data set.
9. The enhanced positioning method based on a TDOA complex scene according to any one of claims 1 to 8, wherein the system based on the enhanced positioning method based on a TDOA complex scene includes a positioning server, a positioning tag and a plurality of base stations, wherein the positioning tag is configured to transmit UWB positioning data; the base station receives the positioning data sent by the positioning tag, records and processes the arrival time stamp information of the positioning data, and then transmits the positioning data to the positioning server; the positioning server is used for receiving the positioning data transmitted by the base station and running the enhanced positioning method under the TDOA-based complex scene to obtain the position information of the positioning tag.
CN202310287564.7A 2023-03-22 2023-03-22 Enhanced positioning method based on TDOA (time difference of arrival) complex scene Pending CN116390021A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310287564.7A CN116390021A (en) 2023-03-22 2023-03-22 Enhanced positioning method based on TDOA (time difference of arrival) complex scene

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310287564.7A CN116390021A (en) 2023-03-22 2023-03-22 Enhanced positioning method based on TDOA (time difference of arrival) complex scene

Publications (1)

Publication Number Publication Date
CN116390021A true CN116390021A (en) 2023-07-04

Family

ID=86968660

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310287564.7A Pending CN116390021A (en) 2023-03-22 2023-03-22 Enhanced positioning method based on TDOA (time difference of arrival) complex scene

Country Status (1)

Country Link
CN (1) CN116390021A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116761253A (en) * 2023-08-17 2023-09-15 湘江实验室 UWB weighted positioning method based on triangular area

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116761253A (en) * 2023-08-17 2023-09-15 湘江实验室 UWB weighted positioning method based on triangular area
CN116761253B (en) * 2023-08-17 2023-10-20 湘江实验室 UWB weighted positioning method based on triangular area

Similar Documents

Publication Publication Date Title
CN106793086A (en) A kind of indoor orientation method
US11419088B2 (en) Radiation source positioning method based on radio spectrum monitoring big data processing
CN112325883B (en) Indoor positioning method for mobile robot with WiFi and visual multi-source integration
CN102440043A (en) Wireless communication network
US20130203423A1 (en) System and Method for Mobile Location Using Ranked Parameter Labels
CN114189809B (en) Indoor positioning method based on convolutional neural network and high-dimensional 5G observation feature
CN116390021A (en) Enhanced positioning method based on TDOA (time difference of arrival) complex scene
CN113207089A (en) Position fingerprint positioning method based on CSI and crowdsourcing migration self-calibration updating
CN111461251A (en) Indoor positioning method of WiFi fingerprint based on random forest and self-encoder
Li et al. Wireless localization with spatial-temporal robust fingerprints
Nurpeiissov et al. End-to-end sequential indoor localization using smartphone inertial sensors and WiFi
CN110636437A (en) Fingerprint pair-based auxiliary positioning method
CN113068127B (en) Positioning method of NB-IoT terminal
Mukhtar et al. Machine learning-enabled localization in 5g using lidar and rss data
CN112135344B (en) CSI (channel State information) and DCNN (distributed channel neural network) -based equipment-free target positioning method
Zhou et al. IMLours: Indoor mapping and localization using time-stamped WLAN received signal strength
Foliadis et al. Reliable deep learning based localization with csi fingerprints and multiple base stations
CN114222365B (en) Ultra-wideband positioning method, device, computing unit and system based on single base station
Mikhalev et al. Passive emitter geolocation using agent-based data fusion of AOA, TDOA and FDOA measurements
CN108650625A (en) Distributing antenna system localization method based on fast run-up library
CN109874149A (en) Localization method, device and the computer readable storage medium of mobile terminal
KR20230025992A (en) Apparatus and method for indoor positioning of pedestrians
CN110692260B (en) Terminal equipment positioning system and method
Hu et al. Reducing Wi-Fi fingerprint collection based on affinity propagation clustering and WKNN interpolation algorithm
US20070146203A1 (en) Method and apparatus for reducing geolocation ambiguity in signal tracking

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination