TDOA-based time synchronization and positioning methods, systems, electronic devices, and storage media

Abstract

This invention provides a time synchronization and positioning method, system, electronic device, and storage medium based on Time-of-Availability (TDOA). Using a reference base station, a TDOA model is constructed based on the TOA measurements of all base station networks. The TDOA model integrates various random measurement errors into a cost function Q, which is determined by the base station's clock offset and tag location. An initial estimate of the clock offset is obtained by combining the TDOA model and the distance constraints between base stations. This initial estimate of the clock offset is then substituted into the cost function Q, and the initial estimate of the tag location is obtained by minimizing the cost function Q. Based on the initial estimates of the clock offset and tag location, clock offset and tag location are calculated in static or dynamic scenarios. This invention considers random measurement errors when solving for the initial estimate of the tag location, meeting high-precision requirements, and achieving positioning and time synchronization without external hardware synchronization or two-way message interaction.

Description

Technical Field

[0001] This invention relates to the field of time synchronization and positioning technology based on TDOA (Time Difference of Arrival), and particularly to a time synchronization and positioning method, system, electronic device and storage medium based on TDOA. Background Technology

[0002] With the large-scale deployment of communication base stations in indoor scenarios, achieving high-precision indoor positioning and clock synchronization on the network side using ground-based navigation technology has become a research hotspot in the industry.

[0003] Currently, various ground-based navigation technologies have emerged and achieved phased results. For example, in some indoor positioning solutions based on UWB (Ultra-Wideband) time difference ranging, high-precision local oscillators are deployed on the base station side and whitelist base stations are set for the tags to ensure positioning accuracy. However, the hardware cost of this solution is high. In addition, although the BLE (Bluetooth Low Energy) TDOA solution is flexible in deployment and low in cost, the signal delay jitter is large and the positioning accuracy is usually 3 to 5 meters, which is difficult to meet the high-precision requirements. Summary of the Invention

[0004] This invention provides a time synchronization and positioning method, system, electronic device, and storage medium based on TDOA to solve the problems of high hardware cost and difficulty in meeting high precision requirements of existing positioning and clock synchronization methods.

[0005] In a first aspect, the present invention provides a time synchronization and positioning method based on TDOA, applied to a positioning area including multiple base stations, wherein any one of the multiple base stations is used as a reference base station, the method comprising:

[0006] Using the reference base station as a reference, a TDOA model is constructed based on the TOA measurement values ​​of all base station networks. The TDOA model is related to the clock offset of the base station and the tag position of the tag to be located. The tag position is related to random measurement error.

[0007] The TDOA model combines all random measurement errors into a cost function Q, which is determined by the base station's clock offset and tag location.

[0008] The initial estimate of the clock offset is obtained by combining the TDOA model and the distance constraints between base stations;

[0009] Substitute the initial estimate of the clock offset into the cost function Q, and solve for the initial estimate of the tag position by minimizing the cost function Q;

[0010] Calculate the clock offset and tag position in static or dynamic scenes based on initial estimates of clock offset and tag position.

[0011] Secondly, the present invention provides a time synchronization and positioning system based on TDOA, comprising:

[0012] For use in a positioning area including multiple base stations, wherein any one of the multiple base stations is used as a reference base station, the system includes:

[0013] The TDOA model construction module is used to construct a TDOA model based on the reference base station and the TOA measurement values ​​of all base station networks. The TDOA model is related to the clock offset of the base station and the tag position of the tag to be located. The tag position is related to random measurement error.

[0014] The cost function Q building module is used to combine the TDOA model to represent each random measurement error as a cost function Q. The cost function Q is determined by the base station clock offset and tag position.

[0015] The first module for solving the initial estimate is used to solve for the initial estimate of the clock offset by combining the TDOA model and the distance constraints between base stations;

[0016] The second module for solving the initial estimate is used to substitute the initial estimate of the clock offset into the cost function Q, and solve for the initial estimate of the tag position by minimizing the cost function Q;

[0017] The positioning and time synchronization module calculates the clock offset and tag position in static or dynamic scenes based on the initial estimates of the clock offset and tag position.

[0018] Thirdly, the present invention provides an electronic device, the electronic device comprising:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the TDOA-based time synchronization and positioning method described in the first aspect of the present invention.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the TDOA-based time synchronization and positioning method described in the first aspect of the present invention.

[0023] This invention provides a time synchronization and positioning method based on TDOA, applicable to a positioning area including multiple base stations. Any one of the base stations is used as a reference base station. A TDOA model is constructed based on the TOA measurements of all base station networks. The TDOA model is related to the clock offset of the base stations and the tag position of the tag to be located. The tag position is related to random measurement errors. The various random measurement errors are comprehensively represented as a cost function Q, determined by the clock offset of the base stations and the tag position. An initial estimate of the clock offset is obtained by combining the TDOA model and the distance constraints between base stations. The initial estimate of the clock offset is substituted into the cost function Q, and the initial estimate of the tag position is obtained by minimizing the cost function Q. Based on the initial estimates of the clock offset and tag position, the clock offset and tag position are calculated in static or dynamic scenarios.

[0024] The beneficial technical effects of this invention are as follows: the cost function Q is entirely determined by the position of each tag and the clock offset of the base station. First, the initial estimate of the clock offset is obtained, and then the initial estimate of the position of all tags is obtained by minimizing the cost function Q. Furthermore, the tag position is related to random measurement error, so random measurement error (e.g., environmental noise) is fully considered when obtaining the initial estimate of the tag position. This makes the time synchronization and positioning method of this invention have good robustness to environmental noise and also meets the high accuracy requirements. In addition, the base station in this invention is an existing base station, which achieves positioning and time synchronization without the need for external hardware synchronization or two-way message interaction, which has significant advantages over traditional hardware or protocol-dependent solutions. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a time synchronization and positioning method based on TDOA provided in an embodiment of the present invention;

[0027] Figure 2 This is a flowchart of a method for updating clock offset and tag position in a static scene provided by an embodiment of the present invention;

[0028] Figure 3 This is a flowchart of a joint time synchronization and positioning algorithm based on EKF provided in an embodiment of the present invention;

[0029] Figure 4This is a schematic diagram illustrating the change in clock offset estimation error in a static scene obtained from simulation, as provided in this embodiment of the invention.

[0030] Figure 5 This is a schematic diagram illustrating the variation of clock offset estimation error with discrete state time in a dynamic scenario obtained from simulation, as provided in the embodiments of the present invention.

[0031] Figure 6 This is a schematic diagram of a time synchronization and positioning system based on TDOA provided in an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0033] Explanation of reference numerals in the attached figures: 40 - electronic device, 41 - processor, 42 - read-only memory (ROM), 43 - random access memory (RAM), 44 - bus, 45 - I / O interface, 46 - input unit, 47 - output unit, 48 - storage unit, 49 - communication unit. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] TOA (Time of Arrival) signal is a technique used to measure the time required for a signal to travel from the transmitter to the receiver.

[0036] TDOA (Time Difference of Arrival) is a positioning technique that uses the time difference of signal arrival. By measuring the time difference between the arrival of signals from multiple ground-based navigation base stations on the tag, the tag's location can be determined.

[0037] A base station is a device in a ground-based navigation system used to provide tags with base station location, timing, and distance information. When a tag establishes a connection with a base station, it can obtain the base station's location, timing, and distance information, including latitude and longitude, altitude, base station clock, and inter-station pseudorange, etc. This is achieved through signal transmission and measurement between the tag and the base station.

[0038] Base station clock skew refers to the time difference caused by the clocks being out of sync between base stations. In time-based positioning technologies (such as TDOA), base station time skew can lead to positioning errors.

[0039] Figure 1 This is a flowchart illustrating a TDOA-based time synchronization and positioning method according to an embodiment of the present invention. This embodiment is applicable to TDOA-based time synchronization and positioning in a positioning area including multiple base stations. The method can be executed by a TDOA-based time synchronization and positioning device, which can be implemented in hardware and / or software and can be configured in an electronic device. Any one of the multiple base stations is used as a reference base station; for example, the first base station is used as the reference base station.

[0040] like Figure 1 As shown, the time synchronization and positioning method based on TDOA includes:

[0041] S101. Using the reference base station as a reference, construct a TDOA model based on the TOA measurement values ​​of all base station networks. The TDOA model is related to the clock offset of the base station and the tag position of the tag to be located. The tag position is related to random measurement error.

[0042] Consider a two-dimensional positioning scenario: In this embodiment, the base stations can be 5G base stations. Communication exists between base stations and between each base station and the tags. The number of base stations in the positioning scenario is M, and the number of tags to be located is N. Assume the internal clock offset of the nth base station is represented as... Base station 1 is used as the reference base station, therefore .

[0043] The construction process of the TDOA model is as follows:

[0044] The expression for the TOA measurement value of the constructed label is:

[0045] ;

[0046] in, It is the first The base station to the Time-synchronized TOA measurement values ​​of individual tags For the speed of information dissemination, Let i be the clock offset of the i-th base station. Indicates the first The coordinate vectors of each base station (known) This represents the coordinate vector of the t-th label (i.e., the label position, which is unknown). It is the synchronization time difference (unknown) from the t-th tag to the reference base station. This is a random measurement error in TOA measurements without time synchronization. It is the random measurement error of the clock offset inside the base station; the two are combined and expressed as random measurement error. , , .

[0047] It should be noted that the TOA measurement value The measurements were taken without time synchronization between the tag and the base station network, or within the base station network itself.

[0048] In this system model, the joint time synchronization and positioning problem can be defined as: utilizing all TOA measurements from the 5G base station network To simultaneously estimate the clock skew within the base station network. and the label position of the label to be located .

[0049] In order to eliminate the unknown The TDOA (Torrent Address Allocation) value of the tag is constructed by introducing the TDOA value with the first base station as the reference base station. The expression is:

[0050] .

[0051] The TDOA model refers to the measured values ​​of TDOA. The expression, as can be seen, shows the measured value of TDOA. The unknown was eliminated in the expression. .

[0052] S102. Combining the TDOA model, the various random measurement errors are expressed as a cost function Q, which is determined by the base station's clock offset and tag location.

[0053] As mentioned in S101, the tag location is related to random measurement errors. In order to reduce the impact of random measurement errors on positioning, the various random measurement errors are combined and expressed as a cost function Q in conjunction with the TDOA model.

[0054] The TDOA model combines various random measurement errors into a cost function Q, which includes:

[0055] Define intermediate variables , , , ;

[0056] but ;

[0057] Will Converting to matrix form, we obtain the matrix expression for the TDOA of the t-th label. ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] in, This can be interpreted as the TDOA quantity of the t-th tag to M base stations. For geometric propagation time difference, For clock offset, these parameters are all vectors.

[0063] Integrating the relevance vectors of the t-th label, we obtain the TDOA matrix for all labels as follows:

[0064] ;

[0065] ;

[0066] ;

[0067] in, Describes the identity matrix in M-1 dimensions. Represents the geometric propagation delay difference of all labels, a vector. Composed of N M-1 dimensional identity matrices, vector C is used to represent the base station clock offset vector of length M-1. Copy N times, Represents an N×1 column vector consisting entirely of 1s. This represents the Kronecker product.

[0068] Cost function ,in, This indicates modulo.

[0069] Cost function Completely determined by the position of each label and base station offset The decision can be made by minimizing the cost function Q to obtain the location information of all labels.

[0070] S103. Solve for the initial estimate of clock offset by combining the TDOA model and the distance constraints between base stations.

[0071] Specifically, this includes: definitions dimensional difference linear transformation matrix , Each row consists of a single 1, -1, and multiple 0s, meaning there is one 1 and one -1 in each row, and their positions are determined by the base station pair number. Each row corresponds to a base station pair, for example... If a row in the algorithm corresponds to the base station pair (2,3), then the second column element of that row is 1 and the third column element is -1; (Definition) dimensional difference linear transformation matrix , for Ignore the difference matrix in the first column;

[0072] according to The distance constraint between the base station and the ground station yields the following linear constraint conditions:

[0073] ;

[0074] ;

[0075] ;

[0076] in, For the first The base station and the first The distance between base stations It is to integrate all The vector.

[0077] Then obtain the vector that satisfies the linear constraint condition. exist The polyhedron formed by the 3D real vector space; by solving for the geometric center of the inscribed ellipsoid of the maximum volume of the polyhedron, the initial estimate of the clock offset of the base station is obtained. .

[0078] The initial estimate of the clock offset is obtained by finding the geometric center of the inscribed ellipsoid of the maximum volume of the polyhedron. The geometric center can avoid the initialization falling into singular points or local minima. When the number of labels is small and the noise is large, it can effectively improve the convergence, get closer to the true value, and reduce the number of iterations.

[0079] S104. Substitute the initial estimate of the clock offset into the cost function Q, and solve for the initial estimate of the tag position by minimizing the cost function Q.

[0080] First, substituting the initial estimate of the clock offset into the cost function Q, we get: , This is the initial estimate of the clock offset;

[0081] Construct the least squares problem based on the cost function Q described above: ;

[0082] The initial estimate of the label position is obtained during the process of solving the least squares problem.

[0083] S105. Calculate the clock offset and tag position in static or dynamic scenes based on the initial estimates of clock offset and tag position.

[0084] After obtaining initial estimates of the clock offset and tag position, the clock offset and tag position in either static or dynamic scenarios can be calculated based on these initial estimates. Both scenarios involve iterative updates of the parameters. For static scenarios, the initial estimates of the clock offset and tag position serve as the initial values ​​for iterative updates. For dynamic scenarios, the clock offset and tag position output after the iterative update in the static scenario can be used as the initial values ​​for iterative updates in the dynamic scenario, further improving the accuracy of positioning and clock offset.

[0085] In the TDOA-based time synchronization and positioning provided in this embodiment, the cost function Q is entirely determined by the position of each tag and the clock offset of the base station. First, an initial estimate of the clock offset is obtained. Then, the initial estimate of the position of all tags is obtained by minimizing the cost function Q. Since the tag position is related to random measurement errors, random measurement errors (such as environmental noise) are fully considered when obtaining the initial estimate of the tag position. Then, based on the clock offset and the initial estimate of the tag position, the clock offset and tag position in static or dynamic scenarios are calculated. This makes the time synchronization and positioning method of this embodiment robust to environmental noise while also meeting high accuracy requirements. Furthermore, the base station in this embodiment is an existing base station, achieving positioning and time synchronization without the need for external hardware synchronization or two-way message interaction, which has significant advantages compared to traditional hardware- or protocol-dependent solutions.

[0086] Optionally, an alternating iterative maximum likelihood synchronous localization algorithm can be used to update the clock offset and tag position in a static scene. The alternating iterative maximum likelihood synchronous localization algorithm is an algorithm for locating multiple mobile devices or sensor nodes. This algorithm achieves high-precision localization by iteratively optimizing the position estimate of each node to minimize the relative position error between all nodes.

[0087] The steps of the alternating iterative maximum likelihood synchronous localization algorithm include:

[0088] Initialization: Assign an initial position estimate for each label.

[0089] Iterative optimization: In each iteration, the positions of other labels are fixed, and only the position of the current node is optimized. Then, the parameter is fixed again, and the position of the next label is optimized. This process is repeated alternately to minimize the relative position error between all labels. Convergence judgment: The above process is repeated until the position estimation converges or the preset number of iterations is reached, and the final result is output.

[0090] Figure 2 The flowchart shows the method for updating clock offset and tag position in a static scene, as follows: Figure 2 As shown, when the tag is in a static scene, the clock offset and tag position in the static scene are calculated based on the initial estimate of the clock offset and tag position, including:

[0091] Step S61: Parameter initialization. , , ;

[0092] This is the initial estimate of the clock offset. This is the initial estimate of the label location; Indicates the number of iterations; sets That is, setting The value is positive infinity, which ensures that the first iteration... It must be much smaller than This allows for continuous iterative updates.

[0093] Step S62 ;

[0094] Step S63: Iteratively solve for the label position using the BFGS quasi-Newton method. .

[0095] Specifically: for the first Each label is based on the least squares problem. The label position is solved iteratively using the BFGS quasi-Newton method. ;

[0096] Step S64: After all labels have been traversed, update the total cost function. ;

[0097] ;

[0098] Step S65: Update clock offset , ;

[0099] Step S66, Judgment ;

[0100] Determine the absolute value of the difference between the two cost functions. Is it less than the preset iteration precision threshold? If yes, stop the iteration and proceed to step S67; otherwise, set... Proceed to step S62 for the next iteration.

[0101] Step S67: Output the joint estimation results, i.e., the final tag position and clock offset.

[0102] Fixed initial clock offset For the position of each label Through the least squares problem The label position is solved independently, and the BFGS quasi-Newton method is used for iterative optimization to approximate the objective function using gradient information. The minimum point. Using the updated label position, the minimum value is found through the least squares formula. Update the clock offset vector.

[0103] Among them, the BFGS (Broyden-Fletcher-Goldfarb-Shanno) quasi-Newton method finds a suitable direction by estimating the inverse matrix of the Newton step size, and then updates the parameter values.

[0104] Optionally, when the tag is in a dynamic scene, the clock offset and tag position in the dynamic scene are calculated based on the initial estimate of the clock offset and tag position, including:

[0105] The clock offset and tag position output in the static scene are used as the initial values ​​for iteration in the dynamic scene; an Extended Kalman Filter (EKF) system state model and measurement model in the dynamic scene are constructed; based on the initial values, system state model and measurement model in the dynamic scene, the clock offset and tag position are iteratively updated using the Extended Kalman Filter algorithm.

[0106] Without considering clock drift, based on a clock model with a fixed clock offset, and setting the clock of base station one as the reference clock, the system state model for the joint time synchronization and positioning problem in dynamic scenarios is derived:

[0107] ;

[0108] in, The state transition matrix has indices. Representing the discrete time step, a vector Represent a A 3D state vector describes the parameters estimated and tracked by the Kalman filter. The system state vector includes the tag position coordinates. Speed ​​information and clock deviation within the base station network .vector This represents a random 𝑁×1 model noise vector, describing the uncertainty in the system model.

[0109] Therefore, the dimension of the state vector of the EKF system is: ,in This indicates the number of base stations. The state transition matrix of the system represents the clock offset between the i-th base station at time k and the reference clock of the reference base station (the first base station). As shown below, where Δt is the interval between two consecutive location requests for the tag.

[0110] Then the state transition matrix ;

[0111] System state transition matrix ;

[0112] In the state transition matrix In the text, the first two lines indicate the label position. As speed changes with time The change is indicated in lines three and four, where the speed is a constant. This indicates that the clock offset is constant.

[0113] The EFK measurement model is defined as follows:

[0114] ;

[0115] in, It is the TDOA measurement value, function This represents the transfer function of a nonlinear measurement model. This represents additional measurement noise with the following statistical characteristics: zero mean, constant variance, and no correlation.

[0116] The EFK noise-free measurement model is defined as follows:

[0117] ;

[0118] Due to the measurement function It is nonlinear and cannot be directly used to estimate the measurement noise covariance matrix; therefore, Linearization, in the prior estimate Perform a first-order Taylor expansion at this point:

[0119] ;

[0120] matrix Represents the measurement model function vector The Jacobian matrix is ​​defined as follows:

[0121] ;

[0122] Based on the established EKF state and measurement models, the prior estimates of the extended Kalman filter are predicted using the state estimation at time n−1.

[0123] ;

[0124] The covariance matrix of the prior estimates is as follows:

[0125] ;

[0126] The prior estimate of EKF is corrected based on the received TDOA measurements, and the residuals of the measurements are as follows:

[0127] ;

[0128] The covariance matrix of the measured residuals can then be obtained as follows:

[0129] ,in, To measure the noise covariance matrix;

[0130] The near-optimal Kalman filter gain is obtained by the following formula:

[0131] ;

[0132] From the above equation, the corrected optimal estimate is as follows:

[0133] ;

[0134] Simultaneously update the covariance matrix of the optimal estimate:

[0135] .

[0136] The prediction-update loop is repeated for each frame of motion data received, and the terminal position and clock synchronization parameters are output in real time. The flowchart of the joint time synchronization and positioning algorithm based on EKF in this embodiment of the invention is as follows: Figure 3 As shown.

[0137] The simulation process is as follows: Four base stations and N static tags are deployed in a 100 m × 100 m plane. 1000 sets of random tag locations and TOA observations are generated (noise standard deviation σ is taken as 1, 2, 4, and 6, respectively, in ns). Then, an alternating iterative maximum likelihood algorithm is run. The difference between the base station clock offset estimate and the true value is compared after each iteration, and the root mean square error (RMSE) is calculated. Figure 4 This is a schematic diagram illustrating the change in clock offset estimation error in a static scenario obtained from the simulation, as shown below. Figure 4 As shown, the results show that when the number of labels N≥20 and σ≤4 ns, the convergence can be stably reduced to below 4ns.

[0138] In the same scenario, only one tag moving at a speed varying from 0.8 to 1.2 m / s is retained. TDOA measurements are received in real time for each frame, and an EKF prediction-update loop is executed. The clock offset error RMSE at each discrete moment during the entire simulation process is statistically analyzed. Figure 5 This is a schematic diagram illustrating how the clock offset estimation error changes with discrete time points in a dynamic scenario obtained from simulation. Figure 5 As shown, the results indicate that under the condition of σ≤4 ns, the error can be stabilized within 3ns. The four curves are the results under the standard deviation of TOA measurement with an additional standard deviation of σ=1,2,4,6σ. The trend of each curve is that as the discrete state time progresses, the clock offset estimation error RMSE value oscillates and decreases, indicating that the state estimation accuracy is improving over time.

[0139] This invention achieves high-precision clock synchronization within 4 ns in static scenarios and within 3 ns in dynamic scenarios through purely wireless signal processing, without requiring any external hardware synchronization or two-way message interaction. It also stably controls positioning accuracy at the sub-meter level (within 2 m in static scenarios and within 1 m in dynamic scenarios), which has significant advantages over traditional solutions that rely on hardware or protocols. Meanwhile, existing 3GPP Release 16 5G positioning technology achieves sub-meter positioning by measuring uplink time difference of arrival (Uplink-TDOA) and downlink positioning reference signal (Downlink Positioning Reference Signal) in conjunction with a centralized synchronization protocol. However, its reliance on synchronization gateways or GPS leads to complex architecture, increased costs, and operational overhead. The method of this invention only requires uplink TDOA observation to complete the entire estimation process. All operations are executed in parallel on the network side without the need for terminal modification, which reduces communication bandwidth, computing and operation and maintenance costs. In addition, this method provides a unified static / dynamic integrated algorithm framework, which can reuse core modules in different scenarios. It also has good robustness and scalability to environmental noise, number of tags and motion models, and fully meets the application requirements of complex indoor multipath environments and moving target tracking.

[0140] This invention also provides a time synchronization and positioning system based on TDOA, used in a positioning area including multiple base stations, where any one of the multiple base stations is used as a reference base station, such as... Figure 6 As shown, the system includes:

[0141] The TDOA model construction module is used to construct a TDOA model based on the reference base station and the TOA measurement values ​​of all base station networks. The TDOA model is related to the clock offset of the base station and the tag position of the tag to be located. The tag position is related to random measurement error.

[0142] The cost function Q building module is used to combine the TDOA model to represent each random measurement error as a cost function Q. The cost function Q is determined by the base station clock offset and tag position.

[0143] The first module for solving the initial estimate is used to solve for the initial estimate of the clock offset by combining the TDOA model and the distance constraints between base stations;

[0144] The second module for solving the initial estimate is used to substitute the initial estimate of the clock offset into the cost function Q, and solve for the initial estimate of the tag position by minimizing the cost function Q;

[0145] The positioning and time synchronization module calculates the clock offset and tag position in static or dynamic scenes based on the initial estimates of the clock offset and tag position.

[0146] Optionally, the TDOA model building module is used to perform the following steps:

[0147] The expression for the TOA measurement value of the constructed label is:

[0148] ;

[0149] in, It is the first The base station to the Time-synchronized TOA measurement values ​​of individual tags For the speed of information dissemination, Let i be the clock offset of the i-th base station. Indicates the first The coordinate vectors of each base station, This represents the coordinate vector of the t-th label. It is the synchronization time difference from the t-th tag to the reference base station. This is a random measurement error in TOA measurements without time synchronization. It is the random measurement error of the clock offset inside the base station; the two are combined and expressed as random measurement error. , , ;

[0150] Using the first base station as the reference base station, construct the TDOA measurement value of the tag. The expression is:

[0151] .

[0152] Optionally, the cost function Q building module is used to perform the following steps:

[0153] definition , , , ;

[0154] but ;

[0155] Will Converting to matrix form, we obtain the matrix expression for the TDOA of the t-th label. ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] Integrating the relevance vectors of the t-th label, we obtain the TDOA matrix for all labels as follows:

[0161] ;

[0162] ;

[0163] ;

[0164] in, Describes the identity matrix in M-1 dimensions. Represents the geometric propagation delay difference of all labels, a vector. Composed of N M-1 dimensional identity matrices, vector C is used to represent the base station clock offset vector of length M-1. Copy N times, Represents an N×1 column vector consisting entirely of 1s. Represents the Kronecker product;

[0165] Cost function .

[0166] Optionally, the initial estimate first solution module is used to perform the following steps:

[0167] definition dimensional difference linear transformation matrix , Each line consists of a single 1, -1, and multiple 0s;

[0168] definition dimensional difference linear transformation matrix , for Ignore the difference matrix in the first column;

[0169] according to The distance constraint between the base station and the ground station yields the following linear constraint conditions:

[0170] ;

[0171] ;

[0172] ;

[0173] in, For the first The base station and the first The distance between base stations It is to integrate all ;

[0174] Obtain the vector that satisfies the linear constraint condition. exist A polyhedron formed by a 3D real vector space;

[0175] The geometric center of the inscribed ellipsoid of the maximum volume of the polyhedron is determined to obtain an initial estimate of the clock offset of the base station.

[0176] Optionally, the second solution module for the initial estimate is used to perform the following steps, including:

[0177] Substituting the initial estimate of the clock offset into the cost function Q, we get: , This is the initial estimate of the clock offset;

[0178] Construct the least squares problem based on the cost function Q described above: ;

[0179] The initial estimate of the label position is obtained during the process of solving the least squares problem.

[0180] Optionally, when the tag is in a static scene, the positioning and time synchronization module is used to perform the following steps:

[0181] S61, Parameter initialization, , , ; This is the initial estimate of the clock offset. This is the initial estimate of the label location. This represents the number of iterations.

[0182] S62, ;

[0183] S63, regarding the first Each label is based on the least squares problem. The label position is solved iteratively using the BFGS quasi-Newton method. ;

[0184] S64. When all labels have been traversed, update the total cost function. , ;

[0185] S65, Update , ;

[0186] S66. Determine the absolute value of the difference between the two cost functions. If the value is less than the preset iteration precision threshold, stop the iteration and output the final tag position and clock offset; otherwise, set... Move to S62 for the next iteration.

[0187] Optionally, when the tag is in a dynamic scene, the positioning and time synchronization module is used to perform the following steps:

[0188] The clock offset and tag position output in the static scene are used as the initial values ​​for iteration in the dynamic scene;

[0189] Construct state and measurement models for an extended Kalman filter system in dynamic scenarios;

[0190] Based on the initial values, system state model, and measurement model in the dynamic scenario, the clock offset and tag position are iteratively updated using the extended Kalman filter algorithm.

[0191] The time synchronization and positioning device based on TDOA provided in the embodiments of the present invention can execute the time synchronization and positioning method based on TDOA provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0192] Figure 7 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device 40 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0193] like Figure 7As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0194] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0195] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the TDOA-based time synchronization and positioning method.

[0196] In some embodiments, the TDOA-based time synchronization and positioning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the TDOA-based time synchronization and positioning method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the TDOA-based time synchronization and positioning method by any other suitable means (e.g., by means of firmware).

[0197] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0198] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0199] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0200] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a monitor with a cathode ray tube or liquid crystal display); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0201] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0202] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0203] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A time synchronization and positioning method based on TDOA, characterized in that, The method is applied to a positioning area including multiple base stations, using any one of the multiple base stations as a reference base station, specifically including: Using the reference base station as a reference, a TDOA model is constructed based on the TOA measurement values ​​of all base station networks. The TDOA model is related to the clock offset of the base station and the tag position of the tag to be located. The tag position is related to random measurement error. The TDOA model combines all random measurement errors into a cost function Q, which is determined by the base station's clock offset and tag location. The initial estimate of the clock offset is obtained by combining the TDOA model and the distance constraints between base stations; Substitute the initial estimate of the clock offset into the cost function Q, and solve for the initial estimate of the tag position by minimizing the cost function Q; Calculate clock offset and tag position in static or dynamic scenes based on initial estimates of clock offset and tag position; The construction process of the TDOA model is as follows: The TOA measurement value for constructing the label is expressed as: ； in, It is the first Time-of-Age (TOA) measurements from base station to tag t without time synchronization For the speed of information dissemination, Let i be the clock offset of the i-th base station. Indicates the first The coordinate vector of each base station This represents the coordinate vector of the t-th label. It is the synchronization time difference from the t-th tag to the reference base station. This is a random measurement error in TOA measurements without time synchronization. It is a random measurement error of the clock offset inside the base station. and Combined as random measurement error , , , Indicates the number of base stations. Indicates the number of tags; Using the first base station as the reference base station, the tag's TDOA measurement value Represented as: 。 2. The method according to claim 1, characterized in that, The combined TDOA model represents each random measurement error as a cost function Q, including: Define intermediate variables , , , ; but ; Will Converting to matrix form, we obtain the matrix expression for the TDOA of the t-th label. ; ； ； ； ； Integrating the relevance vectors of the t-th label, we obtain the TDOA matrix for all labels as follows: ； ； ； in, This represents the geometric propagation delay difference of all tags. Represents an N×1 column vector consisting entirely of 1s. Describes the identity matrix in M-1 dimensions. Represents the Kronecker product; Cost function ,in, This indicates modulo.

3. The method according to claim 2, characterized in that, The initial estimate of the clock offset obtained by combining the TDOA model and the distance constraints between base stations includes: definition dimensional difference linear transformation matrix , Each line consists of a single 1, -1, and multiple 0s; definition dimensional difference linear transformation matrix , for Ignore the difference matrix in the first column; according to The distance constraint between the base station and the ground station yields the following linear constraint conditions: ； ； ； in, For the first The base station and the first The distance between base stations It is to integrate all ; Obtain the vector that satisfies the linear constraint condition. exist A polyhedron formed by a 3D real vector space; The geometric center of the inscribed ellipsoid of the maximum volume of the polyhedron is determined to obtain an initial estimate of the clock offset of the base station.

4. The method according to claim 2 or 3, characterized in that, The step of substituting the initial estimate of the clock offset into the cost function Q and solving for the initial estimate of the tag position by minimizing the cost function Q includes: Substituting the initial estimate of the clock offset into the cost function Q, we get: , This is the initial estimate of the clock offset; Construct the least squares problem based on the cost function Q above: ; The initial estimate of the label position is obtained during the process of solving the least squares problem.

5. The method according to claim 4, characterized in that, When the tag is in a static scene, the clock offset and tag position in the static scene are calculated based on the initial estimate of the clock offset and tag position, including: Step S61, parameter initialization, , , , This is the initial estimate of the clock offset. This is the initial estimate of the label location. Indicates the number of iterations; Step S62, ; Step S63, for the first Each label is based on the least squares problem. The label position is solved iteratively using the BFGS quasi-Newton method. ; Step S64: After all labels have been traversed, update the total cost function. , ; Step S65, Update , ; Step S66: Determine the absolute value of the difference between the two cost functions. If the value is less than the preset iteration precision threshold, stop the iteration and output the final tag position and clock offset; otherwise, set... Proceed to step S62 for the next iteration.

6. The method according to claim 5, characterized in that, When the tag is in a dynamic scene, the clock offset and tag position in the dynamic scene are calculated based on the initial estimate of the clock offset and tag position, including: The clock offset and tag position output in the static scene are used as the initial values ​​for iteration in the dynamic scene; Construct state and measurement models for an extended Kalman filter system in dynamic scenarios; Based on the initial values, system state model, and measurement model in the dynamic scenario, the clock offset and tag position are iteratively updated using the extended Kalman filter algorithm.

7. A time synchronization and positioning system based on TDOA, characterized in that, The system, applicable to a positioning area including multiple base stations, uses any one of the multiple base stations as a reference base station, and includes: The TDOA model construction module is used to construct a TDOA model based on the TOA measurement values ​​of all base station networks, with the reference base station as a reference. The TDOA model is related to the clock offset of the base station and the tag position of the tag to be located. The tag position is related to random measurement error. The cost function Q building module is used to combine the TDOA model to represent each random measurement error as a cost function Q. The cost function Q is determined by the base station clock offset and tag position. The first module for solving the initial estimate is used to solve for the initial estimate of the clock offset by combining the TDOA model and the distance constraints between base stations; The second module for solving the initial estimate is used to substitute the initial estimate of the clock offset into the cost function Q, and solve for the initial estimate of the tag position by minimizing the cost function Q; The positioning and time synchronization module calculates the clock offset and tag position in static or dynamic scenes based on the initial estimates of the clock offset and tag position. The TDOA model building module is used to perform the following steps: The expression for the TOA measurement value of the constructed label is: ； in, It is the first The base station to the Time-synchronized TOA measurement values ​​of individual tags For the speed of information dissemination, Let i be the clock offset of the i-th base station. Indicates the first The coordinate vector of each base station This represents the coordinate vector of the t-th label. It is the synchronization time difference from the t-th tag to the reference base station. This is a random measurement error in TOA measurements without time synchronization. It is the random measurement error of the clock offset inside the base station; the two are combined and expressed as random measurement error. , , ; Using the first base station as the reference base station, construct the TDOA measurement value of the tag. The expression is: 。 8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the time synchronization and positioning method based on TDOA as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the time synchronization and positioning method based on TDOA as described in any one of claims 1-6.

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