Time synchronization and positioning method and system based on TDOA, electronic equipment and storage medium

Abstract

The invention provides a TDOA-based time synchronization and positioning method and system, an electronic device and a storage medium, and the method comprises the steps: taking a reference base station as a reference, and constructing a TDOA model according to TOA measurement values of all base station networks; comprehensively representing each random measurement error as a cost function Q by combining a TDOA model, wherein the cost function Q is determined by the clock offset and the label position of the base station; solving an initial estimation value of the clock offset by combining a TDOA model and a constraint condition of the distance between the base stations; and substituting the initial estimation value of the clock skew into the cost function Q, solving the initial estimation value of the label position by minimizing the cost function Q, and calculating the clock skew and the label position in the static scene or the dynamic scene based on the initial estimation values of the clock skew and the label position. According to the method, the random measurement error is considered when the initial estimation value of the label position is solved, the high-precision requirement can be met, and meanwhile positioning and time synchronization can be achieved without external hardware synchronization or bidirectional message interaction.

Description

Technical Field

[0001] The present invention relates to the technical field of time synchronization and positioning based on TDOA (Time Difference of Arrival), and in particular to a time synchronization and positioning method, system, electronic device and storage medium based on TDOA. Background Art

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

[0003] At present, various ground-based navigation technologies have emerged and achieved phased results. For example, in some indoor positioning solutions based on UWB (UltraWideband) time difference ranging, positioning accuracy is guaranteed by deploying high-precision local oscillators on the base station side and setting up whitelist base stations for tags, but the hardware cost of this solution is high. In addition, although the BLE (Bluetooth Low Energy) TDOA solution is flexible to deploy and low in cost, it has large signal delay jitter and positioning accuracy is usually 3 to 5 meters, which is difficult to meet high-precision requirements. Summary of the Invention

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

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

[0006] Taking 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 the random measurement error.

[0007] Combined with the TDOA model, each random measurement error is comprehensively expressed as a cost function Q, which is determined by the base station's clock offset and tag position;

[0008] Combine the TDOA model and the distance constraint between base stations to solve the initial estimate of the clock offset;

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

[0010] The clock offset and tag position are calculated based on the initial estimates of the clock offset and tag position in static or dynamic scenarios.

[0011] In a second aspect, 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, using any one of the multiple base stations as a reference base station, the system comprising:

[0013] A 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, wherein the TDOA model is related to the clock offset of the base station and the tag position of the tag to be located, and the tag position is related to the random measurement error;

[0014] The cost function Q construction module is used to combine the TDOA model to comprehensively express 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 initial estimate solution module is used to solve the initial estimate of the clock offset by combining the TDOA model and the distance constraint between base stations;

[0016] The second initial estimate solving module is used to substitute the initial estimate of the clock offset into the cost function Q and solve 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 scenarios based on the initial estimated values ​​of the clock offset and tag position.

[0018] In a third aspect, the present invention provides an 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, and the computer program is executed by the at least one processor so that the at least one processor can execute the TDOA-based time synchronization and positioning method described in the first aspect of the present invention.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which are used to enable a processor to implement the TDOA-based time synchronization and positioning method described in the first aspect of the present invention when executed.

[0023] The present invention provides a time synchronization and positioning method based on TDOA, which is applied to a positioning area including multiple base stations. Any base station among the multiple base stations is used as a reference base station. With the reference base station as a reference, a TDOA model is constructed according to 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, and the tag position is related to the random measurement error. In combination with the TDOA model, each random measurement error is comprehensively expressed as a cost function Q, and the cost function Q is determined by the clock offset of the base station and the tag position. The TDOA model is combined with the distance constraint between base stations to solve the initial estimate of the clock offset. The initial estimate of the clock offset is substituted into the cost function Q, and the initial estimate of the tag position is solved by minimizing the cost function Q. The clock offset and tag position in static or dynamic scenarios are calculated based on the initial estimate of the clock offset and the tag position.

[0024] The beneficial technical effects of the present invention are as follows: the cost function Q is completely determined by the position of each tag and the clock offset of the base station. The initial estimated value of the clock offset is first solved, and then the initial estimated values ​​of the positions of all tags are obtained by minimizing the cost function Q. Moreover, the tag position is related to the random measurement error, so the random measurement error (such as environmental noise) is also fully considered when obtaining the initial estimated value of the tag position, so that the time synchronization and positioning method of the present invention has good robustness to environmental noise, while also meeting high-precision requirements; in addition, the base station in the present invention is an existing base station, which realizes positioning and time synchronization without the need for external hardware synchronization or two-way message interaction, which has significant advantages over traditional solutions that rely on hardware or protocols. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 This is a flow chart of a TDOA-based time synchronization and positioning method provided by an embodiment of the present invention;

[0027] Figure 2 This is a flow chart of a method for updating clock offset and label position in a static scenario provided by an embodiment of the present invention;

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

[0029] Figure 4Schematic diagram of the change of clock offset estimation error in a static scenario obtained through simulation according to an embodiment of the present invention;

[0030] Figure 5 Schematic diagram of the variation of clock offset estimation error with discrete state moments in a dynamic scenario obtained through simulation according to an embodiment of the present invention;

[0031] Figure 6 1 is a schematic structural diagram of a TDOA-based time synchronization and positioning system provided by an embodiment of the present invention;

[0032] Figure 7 It is a structural diagram of an electronic device provided by an embodiment of the present invention.

[0033] Explanation of reference numerals: 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 DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

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

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

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

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

[0039] Figure 1 This is a flowchart of a TDOA-based time synchronization and positioning method provided in 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 performed by a TDOA-based time synchronization and positioning device, which can be implemented in hardware and / or software and configured in an electronic device. Any 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 TDOA-based time synchronization and positioning method includes:

[0041] S101. Using a 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 errors.

[0042] Consider a two-dimensional positioning scenario: the base station in this embodiment can be a 5G base station, and each base station communicates with each other and with tags. The number of base stations in the positioning scenario is M, and the number of tags to be positioned is N. Assume that the internal clock offset of the 𝑖th base station is expressed as , taking base station 1 as the reference base station, so .

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

[0044] The expression for constructing the TOA measurement value of the tag is:

[0045] ;

[0046] in, It is Base station to the The TOA measurement value of the tag without time synchronization, is the speed of information transmission, is the clock offset of the i-th base station, Indicates the The coordinate vectors of the base stations (known), Represents the coordinate vector of the t-th label (i.e., the label position, unknown), is the synchronization time difference between the tth tag and the reference base station (unknown), is the random measurement error of the TOA measurement without time synchronization, is the random measurement error of the base station internal clock offset, and the two are combined to express the random measurement error , , .

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

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

[0049] To eliminate the unknown , introduce the TDOA quantity with the first base station as the reference, and construct the TDOA measurement value of the tag with the first base station as the reference base station The expression is:

[0050] .

[0051] TDOA model is the measurement value of TDOA The expression shows that the measured value of TDOA is In the expression, the unknown is eliminated .

[0052] S102. Combine the TDOA model to comprehensively express each random measurement error as a cost function Q, where the cost function Q is determined by the clock offset of the base station and the tag position.

[0053] As mentioned in S101, the tag position is related to the random measurement error. In order to reduce the impact of random measurement error on positioning, the TDOA model is combined to comprehensively represent each random measurement error as a cost function Q.

[0054] Combined with the TDOA model, each random measurement error is comprehensively expressed as a cost function Q, including:

[0055] Defining intermediate variables , , , ;

[0056] but ;

[0057] Will Convert to matrix form and get the matrix expression of TDOA of the tth label ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

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

[0063] Integrate the relevant vectors of the t-th label to obtain the TDOA matrix of all labels:

[0064] ;

[0065] ;

[0066] ;

[0067] in, represents the M-1 dimensional identity matrix, Represents the geometric propagation delay difference of all labels, vector It is composed of N M-1 dimensional unit matrices. The vector C is used to offset the base station clock vector with a length of M-1. Copy N times, represents an N×1 column vector of all 1s, represents the Kronecker product.

[0068] Cost function ,in, Indicates modulus.

[0069] Cost function Completely determined by the position of each label and base station offset Decision. All label position information can be obtained by minimizing the cost function Q.

[0070] S103. Calculate an initial estimate of the clock offset by combining the TDOA model and the distance constraint between base stations.

[0071] Specifically include: Definition dimensional difference linear transformation matrix , Each row of is composed of a single 1, -1 and multiple 0s, that is, the number of 1 and -1 in each row is one, and its position is determined by the number of the base station pair. Each row of corresponds to a base station pair, for example, If the base station pair corresponding to a row in is (2,3), then the second column element of the row is 1 and the third column element is -1; define dimensional difference linear transformation matrix , for Ignore the difference matrix in column 1;

[0072] according to The following linear constraints are obtained by the distance constraints between base stations:

[0073] ;

[0074] ;

[0075] ;

[0076] in, For the Base stations and The distance between base stations, It integrates all vector.

[0077] Then obtain the vector that satisfies the linear constraints exist The polyhedron formed by the dimensional real vector space; solving the geometric center of the ellipsoid inscribed in the maximum volume of the polyhedron to obtain the initial estimate of the base station clock offset .

[0078] The geometric center of the ellipsoid inscribed in the maximum volume of the polyhedron is solved to obtain the initial estimate of the clock offset. The geometric center can prevent the initialization from falling into a singular point or local minimum. When the number of labels is small and the noise is large, it can effectively improve convergence; it is closer to the true value and reduces the number of iterations.

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

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

[0081] According to the above cost function Q, the least squares problem is constructed: ;

[0082] Initial estimates of the label locations are obtained by solving a least squares problem.

[0083] S105 : Calculate the clock offset and the tag position in a static scene or a dynamic scene based on the initial estimated values ​​of the clock offset and the tag position.

[0084] After obtaining initial estimates of clock offset and tag position, these can be used to calculate clock offset and tag position in either static or dynamic scenarios. Both scenarios involve iterative parameter updates. For static scenarios, the initial estimates of clock offset and tag position serve as the initial values ​​for the iterative updates. For dynamic scenarios, the clock offset and tag position output at the completion of the iterative updates in the static scenario serve as the initial values ​​for the iterative updates in the dynamic scenario, further improving positioning and clock offset accuracy.

[0085] In the TDOA-based time synchronization and positioning provided in this embodiment, the cost function Q is completely determined by the position of each tag and the clock offset of the base station. An initial estimate of the clock offset is first obtained, and then the initial estimate of all tag positions is obtained by minimizing the cost function Q. Since the tag positions are related to random measurement errors, random measurement errors (such as environmental noise) are fully considered when obtaining the initial estimate of the tag positions. The clock offset and tag positions in static or dynamic scenarios are then calculated based on the initial estimate of the clock offset and tag positions. This makes the time synchronization and positioning method of this embodiment robust to environmental noise while also meeting high-precision requirements. Furthermore, the base station of 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 offers significant advantages over traditional solutions that rely on hardware or protocols.

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

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

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

[0089] Iterative Optimization: In each iteration, the positions of all other labels are fixed, and only the position of the current node is optimized. Then, the parameters are fixed and the position of the next label is optimized. This process is repeated until the relative position error between all labels is minimized. Convergence Detection: This process is repeated until the position estimate converges or the preset number of iterations is reached, and the final result is output.

[0090] Figure 2 The flowchart of the method for updating clock offset and tag position in static scene is 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 estimated values ​​of the clock offset and tag position, including:

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

[0092] is the initial estimate of the clock offset, is the initial estimate of the label position; Indicates the number of iterations; set , that is, set is positive infinity, which ensures that Must be much smaller than , so that iterative updates can continue.

[0093] Step S62: ;

[0094] Step S63: BFGS quasi-Newton method iteratively solves the label position .

[0095] Specifically: labels, according to the least squares problem , the label position is solved iteratively by the BFGS quasi-Newton method ;

[0096] Step S64: When all labels are traversed, update the total cost function ;

[0097] ;

[0098] Step S65: Update clock offset , ;

[0099] Step S66: Determine ;

[0100] Determine the absolute value of the difference between two cost functions Is it less than the preset iteration accuracy threshold? If yes, stop the iteration and execute step S67; if no, set , go to step S62 and perform the next iteration.

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

[0102] Fixed initial clock offset , for each label position , through the least squares problem Independently solve the label position, use the BFGS quasi-Newton method for iterative optimization, and use gradient information to approximate the objective function Using the updated label position, 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 value.

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

[0105] The clock offset and tag position output in the static scenario are used as the initial values ​​for iteration in the dynamic scenario. An extended Kalman filter (EKF) system state model and measurement model are constructed for the dynamic scenario. 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.

[0106] In this case, clock drift is not considered. Based on the clock model with fixed clock offset, the clock of base station 1 is set as the reference clock. The system state model for the joint time synchronization and positioning problem in dynamic scenarios is derived:

[0107] ;

[0108] in, is the state transition matrix, index Represents a discrete time step, a vector Indicates a dimensional state vector, which describes the parameters estimated and tracked by the Kalman filter. The system state vector includes the tag position coordinates , speed information and the internal clock deviation of the base station network .vector Represents a random 𝑁×1 model noise vector, which describes the uncertainty in the system model.

[0109] Therefore, the dimension of the EKF system state vector is: ,in Indicates the number of base stations. It represents the clock deviation between the i-th base station and the reference clock of the reference base station 1 (the first base station) at the k-th time, and the state transition matrix of the system is As follows, where Δt is the interval between two adjacent positioning requests of the tag.

[0110] Then the state transfer matrix ;

[0111] The state transition matrix of the system ;

[0112] In the state transition matrix The first two lines indicate the label positions. As speed changes with time Change, the third and fourth lines indicate that the speed is constant, Indicates that the clock offset is a constant value.

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

[0114] ;

[0115] in, is the TDOA measurement value, function represents the nonlinear measurement model transfer function, represents the additive measurement noise with the following statistical properties, namely, zero mean, constant variance, and no mutual correlation.

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

[0117] ;

[0118] Since the measurement function is nonlinear and cannot be used directly to estimate the measurement noise covariance matrix. Therefore, Linearization, in the prior estimate Perform a first-order Taylor expansion at:

[0119] ;

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

[0121] ;

[0122] Based on the constructed EKF state model and measurement model, the state estimation at the 𝑘−1 moment is used to predict the prior estimate of the extended Kalman filter:

[0123] ;

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

[0125] ;

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

[0127] ;

[0128] Then the covariance matrix of the measurement residuals can be obtained as:

[0129] ,in, is the measurement noise covariance matrix;

[0130] The near-optimal Kalman filter gain is obtained as follows:

[0131] ;

[0132] According to the above formula, the corrected optimal estimate is as follows:

[0133] ;

[0134] At the same time, the covariance matrix of the optimal estimate is updated:

[0135] .

[0136] The prediction-update cycle is repeated every time a frame of motion data is 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 the embodiment of the present invention is as follows: Figure 3 shown.

[0137] The simulation process is as follows: 4 base stations and N static tags are deployed in a 100 m × 100 m plane. 1000 sets of random tag positions and TOA observations are generated (with noise standard deviations σ set to 1, 2, 4, and 6, respectively, in nanoseconds). Then, an alternating iterative maximum likelihood algorithm is run. The difference between the base station clock offset estimate output at the end of each iteration and the true value is compared to calculate the root mean square error (RMSE). Figure 4 This is a schematic diagram of the change in clock offset estimation error in a static scenario obtained by simulation. Figure 4 As shown in the figure, the results can stably converge to less than 4ns when the number of tags N ≥ 20 and σ ≤ 4ns.

[0138] In the same scenario, only one tag moving at a variable speed of 0.8–1.2 m / s is retained, receiving each frame of TDOA measurement in real time and executing the EKF prediction-update cycle; the RMSE of the clock offset error at each discrete moment in the entire simulation process is calculated. Figure 5 The following is a schematic diagram of the variation of the clock offset estimation error with discrete state moments in a dynamic scenario obtained by simulation. Figure 5 As shown, the results show that under the condition of σ≤4 ns, the error can be stabilized within 3ns. The four curves are the results under the additional standard deviation of TOA measurement value 𝜎=1, 2, 4, and 6𝑛𝑠. The trend of each curve is that as the discrete state moment progresses, the RMSE value of the clock offset estimation error oscillates and decreases, indicating that the state estimation accuracy is improving over time.

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

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

[0141] A 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, wherein the TDOA model is related to the clock offset of the base station and the tag position of the tag to be located, and the tag position is related to the random measurement error;

[0142] The cost function Q construction module is used to combine the TDOA model to comprehensively express 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 initial estimate solution module is used to solve the initial estimate of the clock offset by combining the TDOA model and the distance constraint between base stations;

[0144] The second initial estimate solving module is used to substitute the initial estimate of the clock offset into the cost function Q and solve 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 scenarios based on the initial estimated values ​​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 constructing the TOA measurement value of the tag is:

[0148] ;

[0149] in, It is Base station to the The TOA measurement value of the tag without time synchronization, is the speed of information transmission, is the clock offset of the i-th base station, Indicates the The coordinate vector of the base station, represents the coordinate vector of the t-th label, is the synchronization time difference from the tth tag to the reference base station, is the random measurement error of the TOA measurement without time synchronization, is the random measurement error of the base station internal clock offset, and the two are combined to express the random measurement error , , ;

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

[0151] .

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

[0153] definition , , , ;

[0154] but ;

[0155] Will Convert to matrix form and get the matrix expression of TDOA of the tth label ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] Integrate the relevant vectors of the t-th label to obtain the TDOA matrix of all labels:

[0161] ;

[0162] ;

[0163] ;

[0164] in, represents the M-1 dimensional identity matrix, Represents the geometric propagation delay difference of all labels, vector It is composed of N M-1 dimensional unit matrices. The vector C is used to offset the base station clock vector with a length of M-1. Copy N times, represents an N×1 column vector of all 1s, represents the Kronecker product;

[0165] Cost function .

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

[0167] definition dimensional difference linear transformation matrix , Each row of is composed of single 1, -1 and multiple 0;

[0168] definition dimensional difference linear transformation matrix , for Ignore the difference matrix in column 1;

[0169] according to The following linear constraints are obtained by the distance constraints between base stations:

[0170] ;

[0171] ;

[0172] ;

[0173] in, For the Base stations and The distance between base stations, It integrates all vector of

[0174] Get the vector that satisfies the linear constraints exist The polyhedron formed by the dimensional real vector space;

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

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

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

[0178] According to the above cost function Q, the least squares problem is constructed: ;

[0179] Initial estimates of the label locations are obtained by solving a 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, , , ; is the initial estimate of the clock offset, is the initial estimate of the label position, is the number of iterations;

[0182] S62, ;

[0183] S63, for labels, according to the least squares problem , the label position is solved iteratively by the BFGS quasi-Newton method ;

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

[0185] S65, Update , ;

[0186] S66. Determine the absolute value of the difference between the two cost functions Is it less than the preset iteration accuracy threshold? If so, stop the iteration and output the final tag position and clock offset. If not, set , go to S62 and proceed to 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 label position output in the static scenario are used as the initial values ​​for iteration in the dynamic scenario;

[0189] Construct the state model and measurement model of the extended Kalman filter system in dynamic scenarios;

[0190] According to the initial value, system state model and measurement model in the dynamic scenario, the clock offset and tag position are iteratively updated through the extended Kalman filter algorithm.

[0191] The TDOA-based time synchronization and positioning device provided in the embodiment of the present invention can execute the TDOA-based time synchronization and positioning method 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 can also represent various forms of mobile devices, such as personal digital assistants, 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 examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

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

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

[0195] Processor 41 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 41 executes 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 can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can 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 can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the TDOA-based time synchronization and positioning method in any other suitable manner (e.g., via firmware).

[0197] Various embodiments of the systems and techniques described above 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), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0198] Computer programs for implementing 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 the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

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

[0200] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a monitor having a cathode ray tube or a liquid crystal display) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0201] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0202] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0203] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. 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, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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, and any one of the multiple base stations is used as a reference base station, specifically comprising: Taking 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 the random measurement error. Combined with the TDOA model, each random measurement error is comprehensively expressed as a cost function Q, which is determined by the base station's clock offset and tag position; Combine the TDOA model and the distance constraint between base stations to solve the initial estimate of the clock offset; Substitute the initial estimate of the clock offset into the cost function Q and solve the initial estimate of the tag position by minimizing the cost function Q; The clock offset and tag position are calculated based on the initial estimates of the clock offset and tag position in static or dynamic scenarios.

2. The method according to claim 1, characterized in that The construction process of the TDOA model is: Construct the TOA measurement of the tag, expressed as: ； in, It is The TOA measurement value without time synchronization from the base station to the t-th tag, is the speed of information transmission, is the clock offset of the i-th base station, Indicates the The coordinate vector of the base station, represents the coordinate vector of the t-th label, is the synchronization time difference from the tth tag to the reference base station, is the random measurement error of the TOA measurement without time synchronization, is the random measurement error of the base station's internal clock offset, and The pooling is expressed as random measurement error , , , Indicates the number of base stations, Indicates the number of tags; Taking the first base station as the reference base station, the TDOA measurement value of the tag is Expressed as: 。 3. The method according to claim 2, characterized in that The combined TDOA model comprehensively expresses each random measurement error as a cost function Q, including: Defining intermediate variables , , , ; but ; Will Convert to matrix form and get the matrix expression of TDOA of the tth label ; ； ； ； ； Integrate the relevant vectors of the t-th label to obtain the TDOA matrix of all labels: ； ； ； in, represents the geometric propagation delay difference of all labels, represents an N×1 column vector of all 1s, represents the M-1 dimensional identity matrix, represents the Kronecker product; Cost function ,in, Indicates modulus.

4. The method according to claim 3, characterized in that Solving the initial estimate of the clock offset by combining the TDOA model and the inter-base station distance constraint condition includes: definition dimensional difference linear transformation matrix , Each row of is composed of single 1, -1 and multiple 0; definition dimensional difference linear transformation matrix , for Ignore the difference matrix in column 1; according to The following linear constraints are obtained by the distance constraints between base stations: ； ； ； in, For the Base stations and The distance between base stations, It integrates all vector of Get the vector that satisfies the linear constraints exist The polyhedron formed by the dimensional real vector space; The geometric center of the ellipsoid inscribed in the maximum volume of the polyhedron is solved to obtain an initial estimate of the clock offset of the base station.

5. The method according to claim 3 or 4, characterized in that Substituting the initial estimated value of the clock offset into the cost function Q and solving the initial estimated value 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 obtain: , is the initial estimate of the clock offset; According to the above cost function Q, the least squares problem is constructed: ; Initial estimates of the label locations are obtained by solving a least squares problem.

6. The method according to claim 5, 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 estimated values ​​of the clock offset and tag position, including: Step S61, parameter initialization, , , , is the initial estimate of the clock offset, is the initial estimate of the label position, Indicates the number of iterations; Step S62, ; Step S63, labels, according to the least squares problem , the label position is solved iteratively by the BFGS quasi-Newton method ; Step S64: When all labels are traversed, update the total cost function , ; Step S65, update , ; Step S66: Determine the absolute value of the difference between the two cost functions Is it less than the preset iteration accuracy threshold? If so, stop the iteration and output the final tag position and clock offset. If not, set , go to step S62 and perform the next iteration.

7. The method according to claim 6, 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 estimated values ​​of the clock offset and tag position, including: The clock offset and label position output in the static scenario are used as the initial values ​​for iteration in the dynamic scenario; Construct the state model and measurement model of the extended Kalman filter system in dynamic scenarios; According to the initial value, system state model and measurement model in the dynamic scenario, the clock offset and tag position are iteratively updated through the extended Kalman filter algorithm.

8. A time synchronization and positioning system based on TDOA, characterized in that: Applicable to a positioning area including multiple base stations, any one of the multiple base stations is used as a reference base station, the system comprising: A 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, wherein the TDOA model is related to the clock offset of the base station and the tag position of the tag to be located, and the tag position is related to the random measurement error; The cost function Q construction module is used to combine the TDOA model to comprehensively express 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 initial estimate solution module is used to solve the initial estimate of the clock offset by combining the TDOA model and the distance constraint between base stations; The second initial estimate solving module is used to substitute the initial estimate of the clock offset into the cost function Q and solve 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 scenarios based on the initial estimated values ​​of the clock offset and tag position.

9. An electronic device, characterized in that: The electronic device comprises: 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, and the computer program is executed by the at least one processor so that the at least one processor can execute the TDOA-based time synchronization and positioning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the TDOA-based time synchronization and positioning method according to any one of claims 1 to 7 when executed.

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