TDOA-based Chan-Taylor underground personnel target positioning method, system and equipment

Abstract

The invention discloses a TDOA-based Chan-Taylor underground personnel target positioning method, system and device, and the method comprises the steps: building a reference coordinate system, and deploying N base stations and a target node; a TDOA positioning model is established, and the TDOA positioning model takes one base station as a reference base station, takes the positions of the other two base stations as focuses and takes a distance difference as a long axis; preliminarily estimating the position of an underground target person through a Chan algorithm; based on a preliminary estimation result, estimating the position of the underground personnel by using an improved Chan-Taylor weighting algorithm; an estimation result of the Chan-Taylor weighting algorithm is used as an observed quantity, and a Kalman filter is used for filtering; inputting the filtering result into a target function of a TDOA positioning model, and when the target function is smaller than a preset threshold value, outputting the result as an optimal position estimation value; compared with a traditional method, the method has higher positioning precision and lower calculation complexity, a good positioning effect is achieved, and high practicability and popularization value are achieved.

Description

Technical Field

[0001] The present invention relates to a method, system and device for positioning underground personnel targets, and in particular to a Chan-Taylor method, system and device for positioning underground personnel targets based on TDOA, belonging to the field of intelligent transportation technology. Background Art

[0002] At present, as one of the world's largest coal producers and consumers, my country has become an important energy source and plays a very important role in human production and life. With the rapid development of social economy, all walks of life have made progress to varying degrees. Under this trend, the demand for coal energy has increased significantly and the consumption has increased day by day. With the increase in coal mining output and the deepening of mining depth, the difficulty of mining is also increasing. Under such circumstances, coal mine safety accidents are common. Due to the closed space and complex electromagnetic environment, it is particularly difficult for coal mine equipment and personnel to achieve accurate positioning and navigation in a satellite denial environment. Therefore, the precise positioning of underground personnel is of great significance in my country's mining industry.

[0003] As a mature satellite positioning technology, GPS is suitable for positioning in open-pit coal mine scenarios. However, the scene in the underground tunnel is different from the open-pit coal mine scene. It is difficult for the positioning equipment in the underground tunnel to receive the signal transmitted by the satellite. Therefore, GPS is not suitable for positioning in the underground tunnel. Technologies suitable for positioning in underground tunnels include Ultra WideBand (UWB) positioning, Radio Frequency Identification (RFID) positioning, Wireless Fidelity (Wi-Fi) positioning, and Bluetooth positioning. Among them, the effective transmission distance of RFID technology is short and accurate positioning cannot be achieved; Wi-Fi positioning technology cannot be deployed on a large scale underground and is difficult to implement. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a Chan-Taylor underground personnel target positioning method, system and equipment based on TDOA that can improve positioning accuracy.

[0005] Technical solution: The Chan-Taylor underground personnel target positioning method based on TDOA described in the present invention includes:

[0006] Step 1: Establish a reference coordinate system, deploy N base stations and 1 target node;

[0007] Step 2: Establish a TDOA positioning model, wherein the TDOA positioning model uses one base station as a reference base station, the positions of the other two base stations as focal points, and the distance difference as a major axis;

[0008] Step 3: Preliminarily estimate the location of the target personnel underground using the Chan algorithm;

[0009] Step 4: Based on the preliminary estimation results, the location of the underground personnel is estimated using the improved Chan-Taylor weighted algorithm;

[0010] Step 5: Use the estimation result of the Chan-Taylor weighted algorithm as the observation and filter it using the Kalman filter;

[0011] Step 6: Input the filtering result into the objective function of the TDOA positioning model. When the objective function is less than a preset threshold, output the result as the optimal position estimate.

[0012] Furthermore, the objective function of the TDOA positioning model is:

[0013]

[0014] Where, d Hi represents the distance from the measured position of the target node to base station i minus the distance to the reference base station, z i It represents the distance from the actual location of the target node to base station i minus the distance to the reference base station, i = 1, 2, …, N.

[0015] Furthermore, the step 2 includes: establishing a hyperbola equation with the positions of the two base stations as foci and the distance difference as the major axis:

[0016]

[0017] In the formula, (x, y) represents the measurement position, (x i ,y i ) represents the position of the i-th base station, i = 1, 2, ..., N; when i = 1, it represents the reference base station, x and y represent the horizontal and vertical coordinates of the measurement position, respectively, x i ,y i Respectively represent the horizontal and vertical coordinates of base station i, d i Indicates the distance from the measurement location to base station i, d i,1 It represents the distance from the measured location to base station i minus the distance to the reference base station.

[0018] Furthermore, the step 3 comprises:

[0019] Assume that there are N positioning base stations in the environment, and the distance d from the user to base station i is i for:

[0020]

[0021] Where i = 1, 2, ..., N; x and y represent the horizontal and vertical coordinates of the measurement position, respectively. i ,y i Respectively represent the horizontal and vertical coordinates of base station i, d i represents the distance from the measurement location to base station i;

[0022] make Simplify formula (1):

[0023]

[0024] The distance d from the user to the i-th base station i Divide the distance from the first base station by d1, and record it as d i,1 :

[0025] d i,1 =d i -d1(3)

[0026] By shifting the terms in equation (3) and squaring both sides of the equation, we can obtain:

[0027]

[0028] Combining equation (2) and equation (4), we can get:

[0029]

[0030] Set vector Z = [x, y, d1] T , then formula (5) can be converted to:

[0031] HkD a Z=0(6)

[0032] in,

[0033]

[0034]

[0035] The maximum likelihood estimate of Z is:

[0036]

[0037] in,

[0038] ψ=c 2 BQB

[0039]

[0040]

[0041] Where c is the signal propagation speed, βN is the ranging error of base station N, Q is the ranging error covariance matrix of each base station, and the final estimated value Za can be obtained:

[0042]

[0043] Because the variables in the vector are correlated, there is an error matrix

[0044]

[0045] in,

[0046]

[0047]

[0048]

[0049] Among them, Z a1 , Z a2 and Z a3 Z = [x, y, d1] T Maximum likelihood estimation of vectors x, y and d1;

[0050] By using the weighted least squares method to estimate formula (9), we can obtain the covariance matrix:

[0051] Y=4B'cov(Z a )B'(10)

[0052] in,

[0053] B′=diag(x-x1,y-y2,d1)

[0054]

[0055] Finally, the estimated value of the target position is solved:

[0056]

[0057] Furthermore, the step 4 comprises:

[0058] Step 4.1: Use the initial estimation value of the Chan algorithm as the initial iteration value of the Taylor algorithm to continue estimating the position of the target personnel underground;

[0059] Step 4.2: Perform weighted calculation on the preliminary estimate of the Chan algorithm and the position estimate of the Taylor algorithm to obtain the position estimate.

[0060] Furthermore, the step 4.1 comprises:

[0061] Expand equation (1) according to the Taylor series, ignoring the Taylor series expansion components above the second order, and we get:

[0062] h=GΔ+ε(12)

[0063] In the formula,

[0064]

[0065]

[0066]

[0067] The weighted least squares algorithm is used to estimate equation (12):

[0068] Δ=(G T Q -1 G) -1 G T Q -1 h(13)

[0069] Based on the Taylor algorithm, the result of each iteration is substituted into the next iteration to determine whether it is less than a given threshold until the optimal position estimate is output:

[0070]

[0071] Among them, x0 and y0 are the horizontal and vertical coordinates of the position after each iteration. a and a are the horizontal and vertical coordinates of the optimal position.

[0072] Further, the step 4.2 comprises:

[0073] Calculate the residual of the Chan algorithm's initial estimate and the Taylor algorithm's position estimate.

[0074]

[0075] Where N is the number of base stations, X is the estimated location, and X i represents the coordinates of the i-th base station;

[0076] Normalize the residual to improve positioning accuracy:

[0077]

[0078] In the formula, R k is the weighted coefficient of the kth algorithm, R es is the residual between the solution result and the measured value, N is the number of beacon nodes;

[0079] Perform weighted calculation to obtain the final position estimation result:

[0080]

[0081] Among them, X′ k is the position estimate of the kth algorithm.

[0082] Furthermore, step 5 includes: recursive processing through normal distribution by Kalman filtering algorithm, including: in the first stage, using the position state at the previous moment to predict and estimate the position at the current moment; in the second stage, using the noisy observation value at the current moment to correct the estimated value.

[0083] Based on the same inventive concept, the present invention also provides a Chan-Taylor underground personnel target positioning system based on TDOA, comprising:

[0084] Initialization module, used to establish a reference coordinate system, deploy N base stations and 1 target node;

[0085] A model building module, used to build a TDOA positioning model, wherein the TDOA positioning model uses one base station as a reference base station, the positions of the other two base stations as focal points, and the distance difference as a major axis;

[0086] A preliminary estimation module is used to preliminarily estimate the location of the target personnel underground using the Chan algorithm;

[0087] An optimization estimation module is used to estimate the position of underground personnel based on the preliminary estimation results using an improved Chan-Taylor weighted algorithm;

[0088] A filtering module is used to use the estimation result of the Chan-Taylor weighted algorithm as an observation and filter it using a Kalman filter;

[0089] The positioning module is used to input the filtering result into the objective function of the TDOA positioning model, and when the objective function is less than a preset threshold, output the result as the optimal position estimate.

[0090] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the Chan-Taylor underground personnel target positioning method based on TDOA are implemented according to any one of the above-mentioned items.

[0091] Beneficial effects: Compared with the prior art, the present invention uses UWB positioning, establishes a TDOA positioning model, and combines the Chan-Taylor algorithm to solve the problems of large positioning error, low positioning accuracy, and long positioning time in non-line-of-sight conditions in underground tunnels, thereby improving the positioning accuracy; the present invention improves the Chan algorithm, iterates the initial solution obtained by the Chan algorithm, and the improved Chan-Taylor algorithm can effectively reduce the impact of NLOS errors on positioning results; the present invention uses Kalman filtering to re-process the position coordinates obtained by the Chan-Taylor algorithm, and finally obtains the precise position of the target node. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0093] Figure 2 is a flow chart of the Chan-Taylor algorithm according to an embodiment of the present invention;

[0094] Figure 3 This is a diagram showing the RMSE positioning effect of the Chan algorithm and the Taylor algorithm according to an embodiment of the present invention;

[0095] Figure 4 The CDF curve diagram of the improved Chan algorithm of the embodiment of the present invention;

[0096] Figure 5 This is a CDF curve diagram of the Chan-Taylor algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0097] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0098] As attached Figure 1 As shown, the Chan-Taylor underground personnel target positioning method based on TDOA in this embodiment includes:

[0099] Step 1: Establish a reference coordinate system, deploy N base stations and 1 target node;

[0100] Step 2: Establish a TDOA positioning model, wherein the TDOA positioning model uses one base station as a reference base station, the positions of the other two base stations as focal points, and the distance difference as a major axis;

[0101] Step 3: Preliminarily estimate the location of the target personnel underground using the Chan algorithm;

[0102] Step 4: Based on the preliminary estimation results, the location of the underground personnel is estimated using the improved Chan-Taylor weighted algorithm;

[0103] Step 5: Use the estimation result of the Chan-Taylor weighted algorithm as the observation and filter it using the Kalman filter;

[0104] Step 6: Input the filtering result into the objective function of the TDOA positioning model. When the objective function is less than a preset threshold, output the result as the optimal position estimate.

[0105] Specifically, the present invention establishes a TDOA positioning model by establishing a hyperbola equation with two base station positions as foci and a distance difference as a major axis as follows:

[0106]

[0107] In the formula, (x, y) represents the user terminal location, (x i ,y i ) represents the position of the i-th base station, i = 1, 2, ..., N; d i Indicates the distance between the user and base station i, d i,1 Represents the distance difference between the user and base station i and the reference base station.

[0108] The objective function established by the present invention is as follows:

[0109]

[0110] Where, d Hi It represents the distance from the positioning point H to other base stations minus the distance to the reference base station, which is equivalent to the TDOA measurement value of the positioning point. i It represents the distance from the target location to other base stations minus the distance to the reference base station, which is equivalent to the TDOA measurement value of the desired location point.

[0111] like Figure 2 As shown, the present invention uses the Chan-Taylor algorithm to estimate the position of the underground personnel, and the implementation process is as follows:

[0112] First, assume that there are N = 4 positioning base stations deployed in the positioning environment, and the distance d from the user to each base station is i for:

[0113]

[0114] In the formula, x and y represent the horizontal and vertical coordinates of the user respectively. i ,y i Respectively represent the horizontal and vertical coordinates of the base station, i = 1, 2, ..., N; d iRepresents the distance from the user terminal to base station i.

[0115] make Simplify formula (1):

[0116]

[0117] The distance d from the user to the i-th base station i Divide the distance from the first base station by d1 and record it as d i,1 :

[0118] d i,1 =d i -d1(3)

[0119] By shifting the terms in equation (3) and squaring both sides of the equation, we can obtain:

[0120]

[0121] Combining equation (2) and equation (4), we can get:

[0122]

[0123] Set vector Z = [x, y, d1] T , then formula (5) can be converted to:

[0124] HkD a Z=0(6)

[0125] in,

[0126]

[0127]

[0128] The maximum likelihood estimate of Z is:

[0129]

[0130] in,

[0131] ψ=c 2 BQB

[0132]

[0133]

[0134] Q is the ranging error covariance matrix of each base station, which can be used to obtain the final estimated value Z′a:

[0135]

[0136] Because the variables in the vector are correlated, there is an error matrix

[0137]

[0138] in,

[0139]

[0140]

[0141]

[0142] By using the weighted least squares method to estimate formula (9), we can obtain the covariance matrix:

[0143] Y=4B'cov(Z a )B'(10)

[0144] in,

[0145] B′=diag(x-x1,y-y2,d1)

[0146]

[0147] Finally, solve for an estimate of the target position:

[0148]

[0149] The calculation result of Chan algorithm is used as the initial iteration value of Taylor algorithm to continue estimating the position of the target personnel in the mine:

[0150] Expand equation (1) according to the Taylor series, ignoring the Taylor series expansion components above the second order, and we get:

[0151] h=GΔ+ε (12)

[0152] In the formula,

[0153]

[0154]

[0155]

[0156] The weighted least squares algorithm is used to estimate equation (12):

[0157] Δ=(G T Q -1 G) -1 G T Q -1 h(13)

[0158] The Taylor algorithm substitutes the result of each iteration into the next iteration to determine whether it is less than a given threshold until the optimal position estimate is output:

[0159]

[0160] The position estimation value solved by the Chan algorithm and the position estimation value solved by the Taylor algorithm are both weighted. The specific weighting process is:

[0161] First, calculate the residual between the solution result and the measured value.

[0162]

[0163] Where N is the number of beacon nodes, X is the estimated location, and X i is the coordinate of the i-th beacon node.

[0164] In order to improve the positioning accuracy, the residual is normalized:

[0165]

[0166] In the formula, R k is the weighted coefficient of the kth algorithm, R es is the residual between the solution result and the measured value, and N is the number of beacon nodes.

[0167] Perform weighted calculation to obtain the final position estimation result:

[0168]

[0169] Finally, the estimation result of the Chan-Taylor weighted algorithm is used as the observation and filtered using the Kalman filter. The Kalman filter algorithm performs recursive processing through the normal distribution, and the estimation of the optimal value is divided into two stages. In the first stage, the position state at the previous moment is used to predict the position at the current moment; in the second stage, the estimated value is corrected using the observation value with noise at the current moment. After the prediction result is corrected and updated, the final estimated value of the target node is output by the final estimated state.

[0170] In order to verify the accuracy and feasibility of the method proposed in the present invention, the present invention also conducts simulation experiments for verification. The present invention mainly improves the Chan algorithm. The algorithm simulation process is completed in the Matlab environment. The initial value obtained by the Chan algorithm is substituted into the Taylor algorithm for iteration to obtain the position coordinates of the underground personnel. The present invention compares the Chan algorithm and the improved Chan-Taylor algorithm under the TDOA positioning model. Figure 3As shown, it can be seen that the RMSE of the algorithm after Taylor iteration is smaller, so the obtained position coordinates are more accurate.

[0171] See also Figure 4 , Figure 5 In the present invention, four base stations are established under the TDOA positioning model, with the horizontal axis being the positioning error and the vertical axis being the cumulative distribution function graph of the CDF algorithm. When the vertical axis Y is around 0.7, the horizontal axis X of the improved Chan algorithm is 2, and the horizontal axis X after Chan-Taylor mixed weighting is 1.8. When the vertical axis Y is around 0.8, the horizontal axis X of the improved Chan algorithm is 2.6, and the horizontal axis X after Chan-Taylor mixed weighting is 2.4. It can be seen that even at close CDF values, the horizontal axis corresponding to the Chan-Taylor algorithm curve is significantly smaller than that of the Chan algorithm. This shows that in the simulation environment of the present invention, even at the same probability level, the RMSE of the Chan-Taylor algorithm in target position estimation is significantly smaller than that of the Chan algorithm.

[0172] Based on the same inventive concept, this embodiment also provides a Chan-Taylor underground personnel target positioning system based on TDOA, including:

[0173] Initialization module, used to establish a reference coordinate system, deploy N base stations and 1 target node;

[0174] A model building module, used to build a TDOA positioning model, wherein the TDOA positioning model uses one base station as a reference base station, the positions of the other two base stations as focal points, and the distance difference as a major axis;

[0175] A preliminary estimation module is used to preliminarily estimate the location of the target personnel underground using the Chan algorithm;

[0176] An optimization estimation module is used to estimate the position of underground personnel based on the preliminary estimation results using an improved Chan-Taylor weighted algorithm;

[0177] A filtering module is used to use the estimation result of the Chan-Taylor weighted algorithm as an observation and filter it using a Kalman filter;

[0178] The positioning module is used to input the filtering result into the objective function of the TDOA positioning model, and when the objective function is less than a preset threshold, output the result as the optimal position estimate.

[0179] Based on the same inventive concept, this embodiment also provides a computing device, including: one or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the Chan-Taylor underground personnel target positioning method based on TDOA are implemented according to any one of the above items.

Claims

1. A Chan-Taylor underground personnel target positioning method based on TDOA, characterized in that: include: Step 1: Establish a reference coordinate system, deploy N base stations and 1 target node; Step 2: Establish a TDOA positioning model, wherein the TDOA positioning model uses one base station as a reference base station, the positions of the other two base stations as focal points, and the distance difference as a major axis; Step 3: Preliminarily estimate the location of the target personnel underground using the Chan algorithm; Step 4: Based on the preliminary estimation results, the location of the underground personnel is estimated using the improved Chan-Taylor weighted algorithm; Step 5: Use the estimation result of the Chan-Taylor weighted algorithm as the observation and filter it using the Kalman filter; Step 6: Input the filtering result into the objective function of the TDOA positioning model. When the objective function is less than a preset threshold, output the result as the optimal position estimate.

2. The Chan-Taylor underground personnel target positioning method based on TDOA according to claim 1 is characterized in that: The objective function of the TDOA positioning model is: Where, d Hi represents the distance from the measured position of the target node to base station i minus the distance to the reference base station, z i It represents the distance from the actual location of the target node to base station i minus the distance to the reference base station, i = 1, 2, …, N.

3. The Chan-Taylor underground personnel target positioning method based on TDOA according to claim 2 is characterized in that: The step 2 includes: establishing a hyperbola equation with the positions of the two base stations as the focus and the distance difference as the major axis: In the formula, (x, y) represents the measurement position, (x i ,y i ) represents the position of the i-th base station, i = 1, 2, ..., N; when i = 1, it represents the reference base station, x and y represent the horizontal and vertical coordinates of the measurement position, respectively, x i ,y i Respectively represent the horizontal and vertical coordinates of base station i, d i Indicates the distance from the measurement location to base station i, d i,1 It represents the distance from the measured location to base station i minus the distance to the reference base station.

4. The Chan-Taylor underground personnel target positioning method based on TDOA according to claim 3 is characterized in that: The step 3 comprises: Assume that there are N positioning base stations in the environment, and the distance d from the user to base station i is i for: Where i = 1, 2, ..., N; x and y represent the horizontal and vertical coordinates of the measurement position, respectively. i ,y i Respectively represent the horizontal and vertical coordinates of base station i, d i represents the distance from the measurement location to base station i; make Simplify formula (1): The distance d from the user to the i-th base station i Divide the distance d1 from the first base station and record it as d i,1 : d i,1 =d i -d1 (3) By shifting the terms in equation (3) and squaring both sides of the equation, we can obtain: Combining equation (2) and equation (4), we can get: Set vector Z = [x, y, d1] T , then formula (5) can be converted to: h-G a Z=0 (6) in, The maximum likelihood estimate of Z is: in, ψ=c 2 BQB Where c is the signal propagation speed, β N is the ranging error of base station N, Q is the ranging error covariance matrix of each base station, and the final estimated value Z′a can be obtained: Because the variables in the vector are correlated, there is an error matrix in, Among them, Z a1 , Z a2 and Z a3 Z = [x, y, d1] T Maximum likelihood estimation of vectors x, y and d1; By using the weighted least squares method to estimate formula (9), we can obtain the covariance matrix: Y=4B'cov(Z a )B' (10) in, B′=diag(x-x1,y-y2,d1) Finally, the estimated value of the target position is solved:

5. The Chan-Taylor underground personnel target positioning method based on TDOA according to claim 4 is characterized in that: The step 4 comprises: Step 4.1: Use the initial estimation value of the Chan algorithm as the initial iteration value of the Taylor algorithm to continue estimating the position of the target personnel underground; Step 4.2: Perform weighted calculation on the preliminary estimate of the Chan algorithm and the position estimate of the Taylor algorithm to obtain the position estimate.

6. The Chan-Taylor underground personnel target positioning method based on TDOA according to claim 5 is characterized in that: The step 4.1 comprises: Expand equation (1) according to the Taylor series, ignoring the Taylor series expansion components above the second order, and we get: h=GΔ+ε (12) In the formula, The weighted least squares algorithm is used to estimate equation (12): Δ=(G T Q -1 G) -1 G T Q -1 h (13) Based on the Taylor algorithm, the result of each iteration is substituted into the next iteration to determine whether it is less than a given threshold until the optimal position estimate is output: Among them, x0 and y0 are the horizontal and vertical coordinates of the position after each iteration. a and a are the horizontal and vertical coordinates of the optimal position.

7. The Chan-Taylor underground personnel target positioning method based on TDOA according to claim 6 is characterized in that: The step 4.2 comprises: Calculate the residual of the Chan algorithm's initial estimate and the Taylor algorithm's position estimate. Where N is the number of base stations, X is the estimated location, and X i represents the coordinates of the i-th base station; Normalize the residual to improve positioning accuracy: In the formula, R k is the weighted coefficient of the kth algorithm, R es is the residual between the solution result and the measured value, N is the number of beacon nodes; Perform weighted calculation to obtain the final position estimation result: Among them, X′ k is the position estimate of the kth algorithm.

8. The Chan-Taylor underground personnel target positioning method based on TDOA according to claim 1, characterized in that: The step 5 includes: recursive processing through normal distribution by using the Kalman filter algorithm, including: in the first stage, using the position state at the previous moment to predict and estimate the position at the current moment; in the second stage, using the noisy observation value at the current moment to correct the estimated value.

9. A Chan-Taylor underground personnel target positioning system based on TDOA, characterized in that: include: Initialization module, used to establish a reference coordinate system, deploy N base stations and 1 target node; A model building module, used to build a TDOA positioning model, wherein the TDOA positioning model uses one base station as a reference base station, the positions of the other two base stations as focal points, and the distance difference as a major axis; A preliminary estimation module is used to preliminarily estimate the location of the target personnel underground using the Chan algorithm; An optimization estimation module is used to estimate the position of underground personnel based on the preliminary estimation results using an improved Chan-Taylor weighted algorithm; A filtering module is used to use the estimation result of the Chan-Taylor weighted algorithm as an observation and filter it using a Kalman filter; The positioning module is used to input the filtering result into the objective function of the TDOA positioning model, and when the objective function is less than a preset threshold, output the result as the optimal position estimate.

10. A computing device, characterized in that include: One or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the Chan-Taylor underground personnel target positioning method based on TDOA according to any one of claims 1 to 8 are implemented.

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