A collaborative positioning method, device, equipment, and storage medium

Abstract

The present application proposes a collaborative positioning method, apparatus, device, and storage medium. The method includes: determining an initial positioning estimate of a target to be measured by using an simulated annealing algorithm and a first preset positioning algorithm; screening at least two distance measurement values based on a preset error threshold to obtain target distance measurement values; the at least two distance measurement values are distances obtained by measuring the target to be measured and a target base station at least twice; determining the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values, and the initial positioning estimate.

Description

Technical Field

[0001] The present application relates to positioning, and more particularly to a collaborative positioning method, apparatus, device, and storage medium. Background Art

[0002] With the emergence of the Global Position System (GPS), the need for positioning has become increasingly important in daily life. In traditional positioning algorithms, the Taylor series expansion method is one of the best methods for solving non-linear equations. However, the Taylor algorithm has two disadvantages. One is that it is sensitive to the initial value, and the initial value of the iteration has a great influence on the Taylor algorithm. The second is that the situation of non-convergence may occur. Therefore, how to achieve high-precision positioning of the target to be measured is an urgent problem to be solved. Summary of the Invention

[0003] Embodiments of the present application provide a collaborative positioning method, apparatus, device, and storage medium, which achieve high-precision positioning of the target to be measured.

[0004] Embodiments of the present application provide a collaborative positioning method, including:

[0005] Using a simulated annealing algorithm and a first preset positioning algorithm to determine an initial positioning estimate of the target to be measured;

[0006] Based on a preset error threshold, screening at least two distance measurement values to obtain target distance measurement values; the at least two distance measurement values are distances obtained by measuring the target to be measured and a target base station at least twice;

[0007] Determining the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values, and the initial positioning estimate.

[0008] Embodiments of the present application provide a collaborative positioning apparatus, including:

[0009] A first determination module configured to use a simulated annealing algorithm and a first preset positioning algorithm to determine an initial positioning estimate of the target to be measured;

[0010] A second determination module configured to screen at least two distance measurement values based on a preset error threshold to obtain target distance measurement values; the at least two distance measurement values are distances obtained by measuring the target to be measured and a target base station at least twice;

[0011] A third determination module configured to determine the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values, and the initial positioning estimate.

[0012] Embodiments of the present application provide a device, including: a memory, and one or more processors;

[0013] The memory is configured to store one or more programs;

[0014] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the above embodiments.

[0015] An embodiment of the present application provides a storage medium storing a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented. Description of the Drawings

[0016] Figure 1 is a flowchart of a collaborative positioning method provided by an embodiment of the present application;

[0017] Figure 2 is a schematic diagram showing the range of theoretical distance measurement values provided by an embodiment of the present application;

[0018] Figure 3 is a flowchart of another collaborative positioning method provided by an embodiment of the present application;

[0019] Figure 4 is an error analysis diagram of different algorithms provided by an embodiment of the present application;

[0020] Figure 5 is a schematic diagram comparing the positioning errors of different algorithms provided by an embodiment of the present application;

[0021] Figure 6 is a relationship diagram between the cumulative distribution and the measurement error method provided by an embodiment of the present application;

[0022] Figure 7 is a schematic diagram of the distribution of positioning points provided by an embodiment of the present application;

[0023] Figure 8 is a block diagram of the structure of a collaborative positioning device provided by an embodiment of the present application;

[0024] Figure 9 is a schematic diagram of the structure of a device provided by an embodiment of the present application. Detailed Embodiments

[0025] Embodiments of the present application will be described below with reference to the accompanying drawings.

[0026] In traditional positioning algorithms, the Taylor series expansion method is one of the best methods for solving non-linear equations. The Taylor series expansion method has high solution accuracy and fast iteration speed, making it one of the most commonly used positioning algorithms. The Taylor algorithm has two disadvantages. One is that it is sensitive to the initial value, and the initial value of the iteration has a great influence on the Taylor algorithm. The second is that the situation of non-convergence may occur. The solution is to use multiple algorithms for cooperative positioning. First, use one algorithm to obtain the initial positioning value, and then use this initial value to substitute into the Taylor series expansion method to obtain the exact solution.

[0027] For the Time Difference of Arrival (TDOA) positioning algorithm model, after obtaining multiple TDOA measurement values in time delay estimation, a positioning equation system can be established:

[0028]

[0029] In terms of solving the initial value, generally the Chan algorithm is used to obtain the initial positioning value. When the measurement error follows a Gaussian distribution, the Chan algorithm has accurate positioning and low algorithm complexity. The Chan algorithm uses two-step Weighted Least Squares (WLS). First, assume that the variables are independent of each other, obtain their estimated values, and then consider their mutual relationship to obtain the target position.

[0030]

[0031] Among them, x, y, and R are the estimated values of the coordinates of the target to be measured and the distance from the base station respectively.

[0032] Define the error vector ψ = h - G a Z a , then:

[0033] φ = E[ψψ T ≈ c 2 BQB (3)

[0034] Among them, the first diagonal matrix B = diag{r1, r2,..., r N}, r1, r2,..., r N is the true distance between base station i and the target to be measured, is the covariance matrix of the noise vector following a Gaussian distribution. Assuming that the quantities in Z a are independent of each other, using weighted least squares to obtain:

[0035]

[0036] Since there is a distance between the MS and the base station detector in B, φ is an unknown quantity and further calculations are required.

[0037] In the case where the distance between the target to be measured and the base station is very far, Q can be used instead, and the above formula can be approximated as:

[0038]

[0039] In the case where the distance between the target to be measured and the base station is relatively close, first assume that the target to be measured is very far from the base station, then use the above formula to obtain an initial rough solution. Using this initial solution, the B matrix can be calculated, and then the results of the first and second WLS can be calculated.

[0040] The assumption of the Chan algorithm is based on the measurement error being a zero-mean Gaussian distribution. For measurement values with large errors in the actual environment, such as in an environment with non-line-of-sight errors (NLOS), the performance of this algorithm will decrease significantly.

[0041] In terms of Taylor positioning solution, the positioning accuracy is affected by the distance measurement error and the number of observation equations. The smaller the distance measurement error and the more the number of observation equations, the better the positioning effect. Data with large errors can be eliminated by certain means. At the same time, existing positioning algorithms generally establish observation equations for the distance measurement between the terminal and the base station. In the case where the number of base stations is not large, the number of equations is limited and the positioning effect is average. In view of this, the embodiments of the present application propose a cooperative positioning method, which uses an improved Chan algorithm based on the simulated annealing algorithm and the Taylor series algorithm to perform high-precision positioning on the target to be measured.

[0042] In one embodiment, Figure 1 is a flowchart of a cooperative positioning method provided by the embodiments of the present application. This embodiment is applicable to the case of cooperative positioning of the target to be measured using at least two algorithms. The cooperative positioning method in this embodiment includes S110 - S130.

[0043] S110. Use the simulated annealing algorithm and the first preset positioning algorithm to determine the initial positioning estimate value of the target to be measured.

[0044] In an embodiment, the first preset positioning algorithm is the Chan algorithm. The Chan algorithm is a positioning algorithm based on the TDOA technology with an analytical expression solution, and it has good performance when the TDOA error follows an ideal Gaussian distribution. In the embodiment, the target to be measured refers to the terminal to be measured. For example, the terminal to be measured can be a user equipment (UE) to be located. In the embodiment, the simulated annealing algorithm and the Chan algorithm are used in cooperation to determine the initial positioning estimate of the target to be measured, so as to obtain the accurate positioning position of the target to be measured. The simulated annealing algorithm has the advantages of strong local search ability and short running time. When the distance between the target to be measured and each base station is relatively close, an estimated initial value is also required for the first estimation to solve the estimation matrix of the initial solution. In real life, for example, in the scenario of indoor positioning, the distance between the target to be measured and each base station is relatively close, and at this time, an estimated initial value (i.e., the initial positioning estimate in this embodiment) is required. Therefore, in the embodiment of the present application, the simulated annealing algorithm is introduced into the solving process of the initial positioning solution of the target to be measured to assist the Chan algorithm in performing the initial positioning estimation, that is, to obtain the initial positioning estimate.

[0045] S120. Screen at least two distance measurement values based on a preset error threshold to obtain target distance measurement values.

[0046] In the embodiment, at least two distance measurement values are the distances obtained by measuring the target to be measured and the target base station at least twice. In the embodiment, multiple measurements can be performed between the target to be measured and the target base station to obtain multiple distance measurement values. However, in the actual measurement process, there may be distance measurement values with large errors. To achieve accurate measurement of the target to be measured, a preset error threshold can be configured to screen the distance measurement values, so as to obtain relatively accurate target distance measurement values. The target distance measurement values can be one or multiple, which is related to the configured preset error threshold and the measurement accuracy of the user for the target to be measured. That is, when the user has a high measurement accuracy for the target to be measured, the preset error threshold is configured higher; otherwise, the preset error threshold is configured lower. In the embodiment, the coordinate value of the target base station is the true coordinate value; while the coordinate value of the target to be measured is the initial positioning estimate.

[0047] In the embodiment, according to the coordinate value of the target base station and the coordinate value of the target to be measured, the corresponding distance estimate value can be calculated. The distance estimate value is compared with the distance measurement values obtained by multiple measurements, and the comparison result and the preset error threshold are used to screen the distance measurement values, so as to obtain relatively accurate target distance measurement values.

[0048] S130. Determine the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values, and the initial positioning estimate.

[0049] In an embodiment, the Taylor series algorithm for multiple target sources refers to the Taylor series algorithm that involves the distance measurement values between multiple targets to be measured in the calculation. In an embodiment, by jointly defining based on the Taylor series algorithm for multiple target sources and the Chan algorithm, the position of the target to be measured can be effectively estimated, and it has higher accuracy and is more effective than common algorithms when the error does not follow a Gaussian distribution with zero mean.

[0050] In one embodiment, the simulated annealing algorithm and the first preset positioning algorithm are used to determine the initial positioning estimate value of the target to be measured, including:

[0051] Determine the initial coordinate estimate value of the target to be measured according to the simulated annealing algorithm;

[0052] Based on the first preset positioning algorithm and the initial coordinate estimate value, determine the initial positioning estimate value of the target to be measured.

[0053] In one embodiment, determining the initial coordinate estimate value of the target to be measured according to the simulated annealing algorithm includes:

[0054] Calculate a preset objective function according to the randomly generated initial coordinate value and the distance measurement value, where the distance measurement value is the distance measured between the target to be measured and the target base station;

[0055] Determine the increment value between two preset objective functions corresponding to two randomly generated initial coordinate values respectively;

[0056] When the increment value meets the preset criterion, and the current iteration number reaches the preset iteration number threshold, and the current temperature in the simulated annealing algorithm reaches the termination temperature, use the latest randomly generated initial coordinate value as the initial coordinate estimate value of the target to be measured.

[0057] In one embodiment, the preset criterion includes one of the following:

[0058] When the increment value is less than or equal to zero, accept the latest randomly generated initial coordinate value and reduce the current temperature;

[0059] When the increment value is greater than zero, accept the latest randomly generated initial coordinate value with a first preset probability.

[0060] In one embodiment, determining the initial positioning estimate value of the target to be measured based on the first preset positioning algorithm and the initial coordinate estimate value includes:

[0061] Calculate the first preset diagonal matrix in the first preset positioning algorithm according to the initial coordinate estimate value, where the first preset diagonal matrix is a matrix composed of the true distances between each target base station and the target to be measured;

[0062] Calculate the corresponding first estimate value according to the first preset diagonal matrix and the preset noise vector covariance matrix;

[0063] Obtain the second estimated value based on the first estimated value and a preset estimation error;

[0064] Determine an initial positioning estimate of the target to be measured according to the second estimated value, a second preset diagonal matrix, and known coordinate values of the target base station. The second preset diagonal matrix is a matrix composed of coordinate values of the target to be measured, coordinate values of the target base station, and an estimated value of the distance between the target to be measured and the target base station.

[0065] In an embodiment, the implementation steps of obtaining an initial solution (i.e., the initial positioning estimate in the above embodiment) by the improved Chan algorithm based on the simulated annealing algorithm include:

[0066] Assume that there are N base stations in the venue. For each target to be measured, the preset objective function of the simulated annealing algorithm is set as:

[0067]

[0068] where R i is the estimated value of the distance between the target to be measured and the target base station (the base station with known coordinate values), and R′ i is the measured value of the distance between the target to be measured and the target base station. The meaning of the preset objective function is that the smaller the absolute value of the difference between R i obtained using the estimated coordinates of the target to be measured and the distance measurement value R′ i , the more accurate the estimated coordinates.

[0069] In an embodiment, the steps of the improved Chan algorithm based on the simulated annealing algorithm are as follows:

[0070] Step 1, randomly generate an initial solution ω, and calculate the preset objective function J ω , the current iteration number k = 0, the current temperature t0 = t max , r ∈ (0, 1) is used to control the cooling and annealing. In the embodiment, the initial solution is the randomly generated initial coordinate value in the above embodiment.

[0071] Step 2, perturb to generate a new solution ω′, and calculate the preset objective function J ω′ .

[0072] Step 3, calculate the increment value ΔJ = J ω′ - J ω .

[0073] Step 4, if ΔJ ≤ 0, then accept the new solution ω = ω′, J ω = J ω′ , k = k + 1, reduce the temperature t k = rt k-1, otherwise accept the new solution according to the Metropolis criterion, that is, accept the new solution with the first preset probability (for example, ).

[0074] Step 5: Determine whether the preset iteration number threshold is reached. If the preset iteration number threshold is not reached, continue with Step 2.

[0075] Step 6: Determine whether the termination condition is satisfied. The termination condition is that the termination temperature is reached. If the termination condition is satisfied, output the final result; if the termination condition is not satisfied, reset the iteration number k = 0 and reduce the initial temperature t0 = rt max .

[0076] Step 7: Obtain the initial coordinate estimates (x′, y′).

[0077] Step 8: Use the initial values to calculate the first preset diagonal matrix B in the Chan algorithm, then substitute it into formula (3) to find φ, and then use formula (4) to find the first least squares solution That is, (x0, y0, R0) is obtained.

[0078] Step 9: Since the relationship between x, y, and R is not considered in the first least squares, and this relationship is considered in the second least squares, higher positioning accuracy can be achieved. Use the first estimated values to construct a set of error equations for the second estimation.

[0079]

[0080] Among them, Z i represents the i-th component in Z a , and e i represents the estimation error of Z a .

[0081] Define a new error vector:

[0082] ψ′ = h′ - G′z′ (8)

[0083] Among them:

[0084]

[0085] Among them, (X1, Y1) represents the known coordinates of base station 1.

[0086] Then the covariance matrix of ψ′ is:

[0087]

[0088] Among them, the second preset diagonal matrix is: B′ = diag(x0 - X1, y0 - Y1, R0),

[0089] Similarly estimated using the previous method, we get:

[0090]

[0091] Step 10, obtain the final estimated position

[0092] In the embodiment, the final estimated position Z is the initial positioning estimated value of the target to be measured in the above embodiment.

[0093] In one embodiment, at least two distance measurement values are screened based on a preset error threshold to obtain target distance measurement values, including: determining the distance measurement error value between the initial positioning estimated value of the target to be measured and the target base station; determining the corresponding cumulative distribution function according to the distance measurement error value; determining the corresponding preset error threshold according to the cumulative distribution function; and screening at least two distance measurement values according to the preset error threshold to obtain target distance measurement values.

[0094] In the embodiment, optimizing the Taylor positioning by screening the distance measurement values between the error data threshold and the target to be measured includes the following steps:

[0095] Since the measurement values may have delay errors caused by NLOS or multipath, and the Taylor series expansion algorithm is sensitive to the initial value, after obtaining the initial estimated value, it is necessary to screen out the data with particularly large errors before starting the Taylor algorithm.

[0096] Figure 2 It is a schematic diagram showing the range of theoretical distance measurement values provided by the embodiments of the present application. As Figure 1 shown, A and B are the positions of the base stations, and T is the true position of the target to be measured. Among them, e is the expectation of the measurement error, and the equation of the circle is:

[0097]

[0098]

[0099] Theoretically, the distance measurement values of A and B are between the radius of the large circle and the radius of the small circle. Since an initial value was obtained according to the improved Chan algorithm based on simulated annealing before, the initial value is substituted, the error of each base station from this initial value is calculated, and the cumulative distribution function is calculated. For example, errors above 90% can be removed, which can not only improve the performance to some extent but also screen out some data.

[0100] Suppose there are N base stations and M targets to be measured in the venue. Since the traditional Taylor series expansion algorithm does not take into account the measured distance values between the targets to be measured, some useful information is lost, resulting in a decrease in the positioning accuracy.

[0101] The original Taylor algorithm calculates using the distance relationship between the target to be measured and the base station, that is:

[0102]

[0103] Among them, R i，j represents the measured distance value between the target to be measured and the known base station. To make the positioning more accurate, all position information can be utilized, and the measured distance value between the targets to be measured is added to establish a system of equations.

[0104]

[0105] Among them, (x i , y i ) represents the coordinate value of the target to be measured, (X i , Y i ) represents the coordinate value of the known base station, R' i，j represents the measured distance value between the targets to be measured, and R i，j represents the measured distance value between the target to be measured and the known base station.

[0106] In one embodiment, determining the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement value and the initial positioning estimate value includes: forming a first matrix with the measured distance error value between two targets to be measured and the measured distance error value between the target to be measured and the target base station; forming a second matrix with the difference between the initial positioning estimate value and the estimated coordinate value of the target to be measured; forming a third matrix with the estimated distance value between the target to be measured and the target base station and the previous estimated distance value between two targets to be measured; determining the corresponding fourth matrix based on a preset positioning model and according to the first matrix, the second matrix and the third matrix; performing recursive calculation on the second matrix based on the weighted least squares method, the fourth matrix, the third matrix and a preset covariance matrix until the change amount between the estimated coordinate value on the target to be measured side and the initial positioning estimate value is less than a preset threshold value; using the initial positioning estimate value corresponding to the preset threshold value as the position of the target to be measured.

[0107] In the embodiment, after obtaining the initial solution, it is brought into the improved Taylor series algorithm of multiple target sources, and its characteristics include:

[0108] At the initial value of the target to be measured (That is, at the initial positioning estimate value in the above embodiment, which is the initial positioning estimate value of multiple targets to be measured (1, 2... M) at this time), perform a Taylor series expansion, remove components above the second order, and obtain the following system of equations:

[0109]

[0110] Among them, is the previous estimate of the distance between the targets to be measured, and R i，j is the estimated value of the distance between the target to be measured and the known base station. e i，j is the measurement error of the distance between the targets to be measured, and e' 1，2 is the distance error between the target to be measured and the known base station.

[0111] After sorting, the positioning model is obtained:

[0112] h = GΔ + E (14)

[0113] Among them,

[0114]

[0115] Using the weighted least squares method (WLS) for Equation (14), an estimate of Δ can be obtained:

[0116] Δ = (G T Q -1 G) -1 G T Q -1 h (15)

[0117] Among them, Q represents the covariance matrix of the TDOA measurement values. In the second recursive calculation, let

[0118]

[0119] Repeat the calculation multiple times until Δx i and Δy i are both small enough to meet a certain set threshold value ε:

[0120]

[0121] At this time, the value of (x i , y i ) is the final estimated position. In the embodiment, the value of (x i , y i ) is the position of the target to be measured in the above embodiment.

[0122] In one embodiment, Figure 3It is a flowchart of another collaborative positioning method provided by an embodiment of the present application. As Figure 3 shown, this embodiment includes: S210 - S260.

[0123] S210. Determine the TDOA measurement values.

[0124] In the embodiment, multiple TDOA measurement values between the target to be measured and the target base station are determined.

[0125] S220. Obtain the initial estimated value using the simulated annealing algorithm.

[0126] In the embodiment, based on the simulated annealing algorithm, the initial estimated value of the target to be measured (i.e., the initial coordinate estimated value in the above - mentioned embodiment) is obtained.

[0127] S230. Substitute into the Chan algorithm for short distances to obtain the initial positioning estimated value.

[0128] In the embodiment, by substituting the initial estimated value into the Chan algorithm for short distances, the initial positioning estimated value of the target to be measured can be determined.

[0129] S240. Remove the error data equations.

[0130] In the embodiment, at least two distance measurement values are screened using a preset error threshold to obtain the target distance measurement values. That is, the error data equations refer to the distance measurement values with larger errors.

[0131] S250. Substitute the initial positioning estimated value into the multi - target Taylor algorithm.

[0132] In the embodiment, based on the multi - target Taylor algorithm, the initial positioning estimated value, and the target distance measurement values, the final result, i.e., the position of the target to be measured, can be obtained.

[0133] S260. Output the final result.

[0134] In the embodiment, after obtaining the position of the target to be measured, the position of the target to be measured is output and displayed for the user to refer to.

[0135] In one implementation, 20 targets to be measured with unknown positions and 5 base stations with known positions are randomly placed in a 100m×100m plane. Assume that the distance measurement error follows an exponential distribution with a mean of 10m and a variance of δ 2 = 1. The simulation steps include Step 1 - Step 10.

[0136] Step 1. For each unknown target to be measured i, the objective function of simulated annealing is defined as:

[0137]

[0138] Step 2: For each unknown target to be measured \(i\), perform the following operations:

[0139] 1) Set the iteration termination number to 100, the temperature decrease parameter \(r = 0.98\), and the initial temperature \(t\) max = 100.

[0140] 2) Perturb to generate a new solution \(\omega'\) i , and calculate the objective function

[0141] 3) Calculate the increment

[0142] 4) If \(\Delta J\leq0\), then accept the new solution \(k = k + 1\), decrease the temperature \(t\) k = \(rt\) k-1 , otherwise accept the new solution according to the Metropolis criterion, that is, with a probability accept the new solution.

[0143] 5) Determine whether the termination condition is satisfied. The termination condition is reaching the termination temperature. If satisfied, output the final result. If not, reset the iteration number \(k = 0\) and decrease the initial temperature \(t_0 = rt\) max .

[0144] 6) Obtain the initial value of the coordinate estimate \((x'\) i , \(y'\) i ).

[0145] Step 3: Calculate the matrix \(B\) in the Chan algorithm using the 20 initial values obtained by the simulated annealing algorithm, substitute it into formula (3) to find, and obtain the first least squares solution by solving formula (5) That is, obtain \((x\) 0，i , \(y\) 0，i , \(R\) 0，i ).

[0146] Step 4: Since the relationship between \(x\), \(y\), and \(R\) is not considered in the first least squares, it will be considered in the second least squares, so as to achieve higher positioning accuracy. Use the first estimated value to construct a set of error equations for the second estimation.

[0147]

[0148] Among them, \(Z\) 1，i represents the first component in \(Z\) a，i , and \(e\) i represents the estimation error of \(Z\) a .

[0149] Define a new error vector:

[0150] \(\psi'\) i= h' i - G' i z' i , i = 1, ..., 20

[0151] wherein,

[0152] wherein, (X1, Y1) represents the known coordinates of base station 1.

[0153] Then the covariance matrix of ψ' is:

[0154]

[0155] wherein, B' i = diag(x 0，i - X1, y 0，i - Y1, R 0，i ),

[0156] Similarly, using the previous method for estimation, we get:

[0157]

[0158] Step 5, obtain the Chan algorithm estimated positions of 20 targets to be measured i = 1, ..., 20.

[0159] Step 6, for the initial position estimate Z k , k = 1, ..., 20, calculate the cumulative distribution function of the distance between each base station coordinate and this initial value respectively, i = 1, ..., 5, remove the functions with an error exceeding 90%.

[0160] Step 7, establish the equations:

[0161]

[0162] Step 8, expand at the estimated position obtained by the previous Chan algorithm, and organize to get:

[0163]

[0164]

[0165] Step 9, using the weighted least squares method (WLS), an estimate of Δ can be obtained:

[0166] Δ = (G T Q -1 G T ) -1 GT Q -1 h

[0167] Among them, Q represents the covariance matrix of the TDOA measurement values. In the second recursive calculation, let

[0168]

[0169] The repeated calculation is performed at most 50 times until both Δx_i and Δy_i are small enough.

[0170] Step 10, obtain the final estimated results (x_1, y_1), …, (x_20, y_20). Figure 4 is an error analysis diagram of different algorithms provided by an embodiment of the present application. As Figure 4 shown, the measurement errors obtained by the improved Chan algorithm and the Taylor series algorithm based on the simulated annealing algorithm are the smallest.

[0171] Under other unchanged conditions, analyze the relationship between the variance of the error and the positioning accuracy. Figure 5 is a schematic diagram of the comparison of positioning errors of different algorithms provided by an embodiment of the present application. As Figure 5 shown, the positioning errors obtained by the improved Chan algorithm and the Taylor series algorithm based on the simulated annealing algorithm are the smallest.

[0172] When δ^2 = 0.5, repeat the test 50 times to test the relationship between the positioning error distribution function and the variance. Figure 6 is a relationship diagram between the cumulative distribution and the measurement error method provided by an embodiment of the present application. As Figure 6 shown, the cumulative distribution and the measurement error variance obtained by the improved Chan algorithm and the Taylor series algorithm based on the simulated annealing algorithm are the smallest.

[0173] When the real target is at the point (60, 65), run the algorithm 20 times to obtain the distribution of the positioning points. Figure 7 is a schematic diagram of the distribution of positioning points provided by an embodiment of the present application. As Figure 7 shown, the obtained estimated positioning points are concentrated near the real position of the target to be measured.

[0174] Figure 8 is a structural block diagram of a cooperative positioning device provided by an embodiment of the present application. As Figure 8 shown, the cooperative positioning device in this embodiment includes: a first determination module 310, a second determination module 320, and a third determination module 330.

[0175] The first determination module 310 is configured to determine the initial positioning estimated value of the target to be measured by using the simulated annealing algorithm and the first preset positioning algorithm;

[0176] A second determination module 320, configured to screen at least two distance measurement values based on a preset error threshold to obtain a target distance measurement value; the at least two distance measurement values are distances obtained by measuring the target to be measured and a target base station at least twice.

[0177] A third determination module 330, configured to determine the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement value, and the initial positioning estimate value.

[0178] The collaborative positioning device provided in this embodiment is configured to implement Figure 1 the collaborative positioning method shown in the embodiment. The implementation principle and technical effects of the collaborative positioning device provided in this embodiment are similar and will not be elaborated here.

[0179] In one embodiment, the first determination module 310 includes:

[0180] A first determination unit, configured to determine an initial coordinate estimate value of the target to be measured according to the simulated annealing algorithm;

[0181] A second determination unit, configured to determine an initial positioning estimate value of the target to be measured based on a first preset positioning algorithm and the initial coordinate estimate value.

[0182] In one embodiment, the first determination unit includes:

[0183] A first determination subunit, configured to calculate a preset objective function according to a randomly generated initial coordinate value and a distance measurement value, where the distance measurement value is the distance obtained by measuring the target to be measured and a target base station;

[0184] A second determination subunit, configured to determine an increment value between two preset objective functions corresponding to two randomly generated initial coordinate values respectively;

[0185] A third determination subunit, configured to use the latest randomly generated initial coordinate value as the initial coordinate estimate value of the target to be measured when the increment value meets a preset criterion, and the current iteration number reaches a preset iteration number threshold, and the current temperature in the simulated annealing algorithm reaches the termination temperature.

[0186] In one embodiment, the preset criterion includes one of the following:

[0187] When the increment value is less than or equal to zero, accept the latest randomly generated initial coordinate value and reduce the current temperature;

[0188] When the increment value is greater than zero, accept the latest randomly generated initial coordinate value with a first preset probability.

[0189] In one embodiment, the second determination unit includes:

[0190] A fourth determination subunit, configured to calculate a first preset diagonal matrix in a first preset positioning algorithm according to an initial coordinate estimation value, where the first preset diagonal matrix is a matrix composed of the true distances between each target base station and the target to be measured;

[0191] A fifth determination subunit, configured to calculate a corresponding first estimated value according to the first preset diagonal matrix and a preset noise vector covariance matrix;

[0192] A sixth determination subunit, configured to obtain a second estimated value according to the first estimated value and a preset estimation error;

[0193] A seventh determination subunit, configured to determine an initial positioning estimation value of the target to be measured according to the second estimated value, a second preset diagonal matrix, and the known coordinate values of the target base stations, where the second preset diagonal matrix is a matrix composed of the coordinate values of the target to be measured, the coordinate values of the target base stations, and the distance estimation values between the target to be measured and the target base stations.

[0194] In one embodiment, the second determination module 320 includes:

[0195] A third determination unit, configured to determine a distance measurement error value between an initial positioning estimation value of the target to be measured and a target base station;

[0196] A fourth determination unit, configured to determine a corresponding cumulative distribution function according to the distance measurement error value;

[0197] A fifth determination unit, configured to determine a corresponding preset error threshold according to the cumulative distribution function;

[0198] A sixth determination unit, configured to screen at least two distance measurement values according to the preset error threshold to obtain target distance measurement values.

[0199] In one embodiment, the third determination module 330 includes:

[0200] A seventh determination unit, configured to form a first matrix with the distance measurement error values between two targets to be measured and the distance measurement error values between the target to be measured and the target base stations;

[0201] An eighth determination unit, configured to form a second matrix with the difference between the initial positioning estimation value and the estimated coordinate value of the target to be measured;

[0202] A ninth determination unit, configured to form a third matrix with the distance estimation value between the target to be measured and the target base station and the previous distance estimation value between two targets to be measured;

[0203] A tenth determination unit, configured to determine a corresponding fourth matrix based on a preset positioning model according to the first matrix, the second matrix, and the third matrix;

[0204] A computing unit configured to perform recursive calculation on a second matrix based on a weighted least squares method, a fourth matrix, a third matrix, and a preset covariance matrix until the change amount between the estimated coordinate value of the target to be measured and the initial positioning estimated value is less than a preset threshold value;

[0205] An eleventh determination unit configured to use the initial positioning estimated value corresponding to less than the preset threshold value as the position of the target to be measured.

[0206] In one embodiment, the first preset positioning algorithm is the Chan algorithm.

[0207] Figure 9 It is a schematic structural diagram of a device provided by an embodiment of the present application. As Figure 9 shown, the device provided by the present application includes: a processor 410 and a memory 420. The number of processors 410 in this device can be one or more, Figure 9 Taking one processor 410 as an example. The number of memories 420 in this device can be one or more, Figure 9 Taking one memory 420 as an example. The processor 410 and the memory 420 of this device can be connected through a bus or other means, Figure 9 Taking the connection through a bus as an example. In this embodiment, this device is a computer device.

[0208] The memory 420, as a computer-readable storage medium, can be set to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the device in any embodiment of the present application (for example, the first determination module 310, the second determination module 320, and the third determination module 330 in the cooperative positioning device). The memory 420 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the device, etc. In addition, the memory 420 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 420 can further include a memory remotely set relative to the processor 410, and these remote memories can be connected to the device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0209] The device provided above can be set to execute the cooperative positioning method provided in any of the above embodiments, and has corresponding functions and effects.

[0210] The embodiment of the present application further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute a collaborative positioning method when executed by a computer processor. The method includes: determining an initial positioning estimate value of a target to be measured by using a simulated annealing algorithm and a first preset positioning algorithm; screening at least two distance measurement values based on a preset error threshold to obtain target distance measurement values; the at least two distance measurement values are distances obtained by measuring the target to be measured and a target base station at least twice; determining the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values and the initial positioning estimate value.

[0211] Those skilled in the art should understand that the term user equipment covers any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable network browser or a vehicle-mounted mobile station.

[0212] Generally speaking, various embodiments of the present application can be implemented in hardware or dedicated circuits, software, logic or any combination thereof. For example, some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software that can be executed by a controller, a microprocessor or other computing devices, although the present application is not limited thereto.

[0213] The embodiments of the present application can be implemented by a data processor of a mobile device executing computer program instructions, for example, in a processor entity, or by hardware, or by a combination of software and hardware. The computer program instructions can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages.

[0214] Any block diagram of a logic flow in the attached drawings of this application may represent program steps, or may represent interconnected logic circuits, modules and functions, or may represent a combination of program steps and logic circuits, modules and functions. A computer program may be stored on a memory. The memory may be of any type suitable for the local technical environment and may be implemented using any suitable data storage technology, such as but not limited to Read-Only Memory (ROM), Random Access Memory (RAM), optical memory devices and systems (Digital Video Disc (DVD) or Compact Disk (CD)), etc. The computer-readable medium may include a non-transitory storage medium. The data processor may be of any type suitable for the local technical environment, such as but not limited to general-purpose computers, special-purpose computers, microprocessors, Digital Signal Processing (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FGPA), and processors based on multi-core processor architectures.

Claims

1. A collaborative positioning method, characterized in that, Including: Determine the initial coordinate estimate of the target to be measured according to the simulated annealing algorithm; Calculate the first preset diagonal matrix in the first preset positioning algorithm according to the initial coordinate estimate, where the first preset diagonal matrix is a matrix composed of the true distances between each target base station and the target to be measured; Calculate the corresponding first estimate according to the first preset diagonal matrix and the preset noise vector covariance matrix; Obtain the second estimate according to the first estimate and the preset estimation error; Determine the initial positioning estimate of the target to be measured according to the second estimate, the second preset diagonal matrix, and the known coordinate values of the target base station, where the second preset diagonal matrix is a matrix composed of the coordinate values of the target to be measured, the coordinate values of the target base station, and the estimated distance values between the target to be measured and the target base station; Screen at least two distance measurement values based on a preset error threshold to obtain target distance measurement values; The at least two distance measurement values are the distances obtained by measuring the target to be measured and the target base station at least twice; Determine the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values, and the initial positioning estimate; 2. The method according to claim 1, wherein The determining the initial coordinate estimate of the target to be measured according to the simulated annealing algorithm includes: Calculate a preset objective function according to the randomly generated initial coordinate values and the distance measurement values, where the distance measurement values are the distances obtained by measuring the target to be measured and the target base station; Determine the increment value between the two preset objective functions corresponding to the two randomly generated initial coordinate values respectively; When the increment value meets the preset criterion, the current iteration number reaches the preset iteration number threshold, and the current temperature in the simulated annealing algorithm reaches the termination temperature, use the latest randomly generated initial coordinate values as the initial coordinate estimate of the target to be measured; 3. The method according to claim 2, wherein The preset criterion includes one of the following: When the increment value is less than or equal to zero, accept the latest randomly generated initial coordinate values and reduce the current temperature; When the increment value is greater than zero, accept the latest randomly generated initial coordinate values with a first preset probability; 4. The method according to claim 1, wherein The screening at least two distance measurement values based on a preset error threshold to obtain target distance measurement values includes: Determine the distance measurement error value between the initial positioning estimate of the target to be measured and the target base station; Determine the corresponding cumulative distribution function according to the distance measurement error value; Determine the corresponding preset error threshold according to the cumulative distribution function; Screen at least two distance measurement values according to the preset error threshold to obtain target distance measurement values; 5. The method according to claim 1, characterized in that, The determining the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values, and the initial positioning estimate includes: forming a first matrix with the distance measurement error values between two targets to be measured and the distance measurement error values between the target to be measured and the target base station; Form a second matrix using the difference between the initial positioning estimate of the target to be measured and the estimated coordinate values; Form a third matrix using the estimated distance values between the target to be measured and the target base station and the previous distance estimate values between two targets to be measured; Determine a corresponding fourth matrix based on a preset positioning model and according to the first matrix, the second matrix, and the third matrix; Perform recursive calculation on the second matrix based on the weighted least squares method, the fourth matrix, the third matrix, and a preset covariance matrix until the change amount between the estimated coordinate value of the target to be measured and the initial positioning estimated value is less than a preset threshold; Take the initial positioning estimated value corresponding to less than the preset threshold as the position of the target to be measured.

6. The method according to any one of claims 1-5, characterized in that, The first preset positioning algorithm is the Chan algorithm.

7. A collaborative positioning device, characterized in that, It includes: A first determination module configured to determine an initial coordinate estimated value of the target to be measured according to the simulated annealing algorithm, and calculate a first preset diagonal matrix in the first preset positioning algorithm according to the initial coordinate estimated value, where the first preset diagonal matrix is a matrix composed of the true distances between each target base station and the target to be measured; calculate a corresponding first estimated value according to the first preset diagonal matrix and a preset noise vector covariance matrix; Obtain a second estimated value according to the first estimated value and a preset estimation error; Determine an initial positioning estimated value of the target to be measured according to the second estimated value, a second preset diagonal matrix, and the known coordinate values of the target base stations, where the second preset diagonal matrix is a matrix composed of the coordinate value of the target to be measured, the coordinate values of the target base stations, and the estimated distance values between the target to be measured and the target base stations; A second determination module configured to screen at least two distance measurement values based on a preset error threshold to obtain target distance measurement values; The at least two distance measurement values are the distances obtained by measuring the target to be measured and the target base stations at least twice; A third determination module configured to determine the position of the target to be measured according to the Taylor series algorithm of multiple target sources, the target distance measurement values, and the initial positioning estimated value.

8. A device, characterized in that, It includes: A memory, and one or more processors; The memory is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-6.

9. A storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method according to any one of claims 1-6.

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