WO2021203871A1 - 协同定位方法、装置、设备和存储介质 - Google Patents

协同定位方法、装置、设备和存储介质 Download PDF

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WO2021203871A1
WO2021203871A1 PCT/CN2021/078794 CN2021078794W WO2021203871A1 WO 2021203871 A1 WO2021203871 A1 WO 2021203871A1 CN 2021078794 W CN2021078794 W CN 2021078794W WO 2021203871 A1 WO2021203871 A1 WO 2021203871A1
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target
value
preset
measured
algorithm
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PCT/CN2021/078794
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English (en)
French (fr)
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陈大伟
陈诗军
金玲飞
钱路雁
李俊强
王阳
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中兴通讯股份有限公司
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Priority to US17/785,640 priority Critical patent/US20230077304A1/en
Priority to JP2022535910A priority patent/JP7344389B2/ja
Priority to EP21785628.5A priority patent/EP4134697A4/en
Priority to KR1020227021724A priority patent/KR20220101195A/ko
Publication of WO2021203871A1 publication Critical patent/WO2021203871A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/0244—Accuracy or reliability of position solution or of measurements contributing thereto
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0284—Relative positioning
    • G01S5/0289—Relative positioning of multiple transceivers, e.g. in ad hoc networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/12—Simultaneous equations, e.g. systems of linear equations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound

Definitions

  • This application relates to the field of positioning, for example, to a method, device, device, and storage medium for coordinated positioning.
  • the Taylor series expansion method is one of the best solutions for solving nonlinear equations, but the Taylor algorithm has two shortcomings. The first is that it is more sensitive to the initial value and the initial value of the iteration. It has a greater impact on Taylor's algorithm, and the second is that there may be non-convergence.
  • the present application provides a collaborative positioning method, device, equipment, and storage medium, which realizes high-precision positioning of the target to be measured.
  • the embodiment of the present application provides a coordinated positioning method, including:
  • the simulated annealing algorithm and the first preset positioning algorithm are used to determine the initial positioning estimate of the target to be measured; at least two distance measurement values are screened based on the preset error threshold to obtain the target distance measurement value; the at least two distance measurement values are to be treated
  • the distance obtained by measuring the target and the target base station at least twice; the position of the target to be measured is determined according to the Taylor series algorithm of multiple target sources, the target distance measurement value and the initial positioning estimation value.
  • the embodiment of the present application also provides a cooperative positioning device, including:
  • the first determining module is configured to use the simulated annealing algorithm and the first preset positioning algorithm to determine the initial positioning estimate of the target to be measured; the second determining module is configured to filter at least two distance measurement values based on the preset error threshold to obtain the target Distance measurement value; the at least two distance measurement values are the distance obtained by performing at least two measurements of the target to be measured and the target base station; the third determining module is configured to measure the target distance according to the Taylor series algorithm of the multi-target source Value and the initial positioning estimate value to determine the target position to be measured.
  • An embodiment of the present application also provides a device, including: a memory and one or more processors; the memory is configured to store one or more programs; when the one or more programs are used by the one or more The processor executes, so that the one or more processors implement the aforementioned co-location method.
  • An embodiment of the present application also provides a storage medium that stores a computer program, and when the computer program is executed by a processor, the aforementioned co-location method is implemented.
  • FIG. 1 is a flowchart of a method for co-location according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of displaying a theoretical distance measurement value range provided by an embodiment of the present application
  • FIG. 3 is a flowchart of another collaborative determination method provided by an embodiment of the present application.
  • FIG. 4 is an error analysis diagram of a different algorithm provided by an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a comparison of positioning errors of different algorithms according to an embodiment of the present application.
  • FIG. 6 is a relationship diagram between a cumulative distribution and a measurement error method provided by an embodiment of the present application.
  • FIG. 7 is a schematic diagram of a positioning point distribution provided by an embodiment of the present application.
  • FIG. 8 is a structural block diagram of a cooperative positioning device provided by an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a device provided by an embodiment of the present application.
  • Taylor series expansion method is one of the best solutions for solving nonlinear equations.
  • Taylor series expansion method has higher solution accuracy and faster iteration speed, making it the most commonly used positioning method.
  • the Taylor algorithm has two shortcomings. The first is that it is more sensitive to the initial value. The initial value of the iteration has a greater impact on the Taylor algorithm. The second is that it may not converge.
  • the solution is to use multiple algorithms for coordinated positioning. An algorithm is used to obtain the initial positioning value, and then the initial value is used to bring it into the Taylor series expansion method to obtain an accurate solution.
  • TDOA Time Difference of Arrival
  • Chan algorithm In terms of initial value solution, Chan algorithm is generally used to obtain the initial value of positioning. When the measurement error of the Chan algorithm obeys the Gaussian distribution, the location of the algorithm is accurate, and the algorithm complexity is not high.
  • the two-step Weighted Least Squares (WLS) adopted by the Chan algorithm first assumes that the variables are independent of each other, obtain their estimated values, and then consider the relationship between them to obtain the target position.
  • x, y and R are respectively the coordinates of the target to be measured and the estimated value of the distance from the base station.
  • Z a is assumed that the amount of the respective independent, obtained by the weighted least squares:
  • ⁇ is an unknown quantity and needs to be calculated.
  • the object to be measured is close to the base station, first assume that the object to be measured is far away from the base station, and then use the above formula to get an initial rough solution, use the initial solution to calculate the B matrix, and then calculate the first time And the result of the second WLS.
  • the assumption of the Chan algorithm is based on the measurement error of zero mean Gaussian distribution. For measurement values with large errors in the actual environment, for example, in an environment with non-line of sight (NLOS) errors, the performance of the algorithm will decrease .
  • NLOS non-line of sight
  • the positioning accuracy is affected by the distance measurement error and the number of observation equations.
  • the positioning algorithm is generally to establish an observation equation for measuring the distance between the terminal and the base station. In the case of a small number of base stations, the number of equations is limited, and the positioning effect is average.
  • an embodiment of the present application proposes a coordinated positioning method, according to the improved Chan algorithm and Taylor series algorithm of the simulated annealing algorithm, to perform high-precision positioning of the target to be measured.
  • FIG. 1 is a flowchart of a cooperative positioning method provided by an embodiment of the present application. This embodiment is applicable to a situation where at least two algorithms are used to perform coordinated positioning of the target to be measured.
  • the coordinated positioning method in this embodiment includes S110-S130.
  • the first preset positioning algorithm is the Chan algorithm.
  • Chan algorithm is a positioning algorithm based on TDOA technology with analytical expression solutions. It performs well when the TDOA error obeys the ideal Gaussian distribution.
  • the target to be tested refers to the terminal to be tested, for example, the terminal to be tested may be a user equipment (User Equipment, UE) to be located.
  • the simulated annealing algorithm and the Chan algorithm are used to jointly determine the initial positioning estimation value 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.
  • an initial estimate value is also required for the first estimate, in order to solve the estimate matrix of the initial solution.
  • an initial estimated value that is, the initial positioning estimated value in this embodiment
  • the simulated annealing algorithm is introduced into the solution process of the initial positioning estimation value of the target to be measured, in order to assist the Chan algorithm in the initial positioning estimation, that is, to obtain the initial positioning estimation value.
  • S120 Filter 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 the distances obtained by performing at least two measurements on the target to be measured and the target base station.
  • multiple measurements can be performed between the target to be measured and the target base station to obtain multiple distance measurement values.
  • a preset error threshold can be configured to filter out the distance measurement value to obtain a more accurate target distance measurement value.
  • the target distance measurement value can be one or multiple, which is related to the configured preset error threshold and the user's measurement accuracy of the target to be measured.
  • the The preset error threshold is configured to be higher; on the contrary, the preset error threshold is configured to be lower.
  • the coordinate value of the target base station is the real coordinate value; and the coordinate value of the target to be measured is the initial positioning estimate value.
  • the corresponding distance estimation value can be calculated, the distance estimation value is compared with the distance measurement value obtained by multiple measurements, and the comparison result is compared with the predicted value.
  • the error threshold is set to filter the distance measurement value, and a more accurate target distance measurement value can be obtained.
  • S130 Determine the position of the target to be measured according to the Taylor series algorithm of the multi-target source, the target distance measurement value and the initial positioning estimate value.
  • the Taylor series algorithm for multiple target sources refers to the Taylor series algorithm that takes the measured values of the distances between multiple targets to be measured into the calculation.
  • the Taylor series algorithm based on multiple target sources and the Chan algorithm are collaboratively defined, which can effectively estimate the position of the target to be measured, and when the error does not obey the Gaussian distribution with zero mean, it is better than the commonly used algorithm. Higher precision and more effective.
  • using the simulated annealing algorithm and the first preset positioning algorithm to determine the initial positioning estimate of the target to be measured includes:
  • the initial coordinate estimation value of the target to be measured is determined according to the simulated annealing algorithm; the initial position estimation value of the target to be measured is determined based on the first preset positioning algorithm and the initial coordinate estimation value.
  • determining the initial coordinate estimation value of the target to be measured according to the simulated annealing algorithm includes:
  • the distance measurement value is the distance measured between the target to be measured and the target base station; determine the two preset targets corresponding to the two randomly generated initial coordinate values.
  • the incremental value between functions; when the incremental 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, the latest randomly generated initial coordinate The value is used as the initial coordinate estimate of the target to be measured.
  • the preset criterion includes one of the following:
  • determining the initial positioning estimate of the target to be measured based on the first preset positioning algorithm and the initial coordinate estimate includes:
  • the first preset diagonal matrix is a matrix composed of the true distance between each target base station and the target to be measured; according to the first preset Suppose the diagonal matrix and the preset noise vector covariance matrix are calculated to obtain the corresponding first estimated value; the second estimated value is obtained according to the first estimated value and the preset estimated error; the second estimated value is obtained according to the second estimated value and the second estimated value.
  • the diagonal matrix and the known coordinate values of the target base station determine the initial positioning estimate of the target to be measured, and the second preset diagonal matrix is based on the coordinate value of the target to be measured, the coordinate value of the target base station, and the target and the target A matrix of distance estimates between base stations.
  • the implementation steps of the improved Chan algorithm based on the simulated annealing algorithm to obtain the initial solution include:
  • the preset objective function of the simulated annealing algorithm is set as:
  • R i is the estimated value of the distance between the target to be measured and the target base station (base station with known coordinate values)
  • R i ′ is the measured value of the distance between the target to be measured and the target base station.
  • the steps of the improved Chan algorithm based on the simulated annealing algorithm are as follows:
  • the initial solution is the initial coordinate value randomly generated in the foregoing embodiment.
  • Step 2 The disturbance generates a new solution ⁇ ', and the preset objective function J ⁇ 'is calculated.
  • the first preset probability e.g.,
  • Step 5 Judge whether the preset number of iterations threshold is reached, and if the preset number of iterations threshold is not reached, continue to step 2.
  • Step 6 Determine whether the termination condition is met.
  • Step 7 Obtain the initial value of coordinate estimation (x', y').
  • Step 8 Use the initial value to calculate the first preset diagonal matrix B in the Chan algorithm, and then use formula (3) to find ⁇ , and then use formula (4) to find the first least square solution That is, (x 0 ,y 0 ,R 0 ) is obtained.
  • Step 9 Since the relationship between x, y and R is not considered in the first least squares, the relationship between these three is considered in the second least squares, thereby achieving higher positioning accuracy.
  • Using the first estimate construct a set of error equations for the second estimate.
  • Z i represents a Z a component of the i
  • e i Z a represents the estimation error
  • (X 1 , Y 1 ) represents the known coordinates of the base station 1.
  • Step 10 get the final estimated position
  • the final estimated position Z is the initial position estimation value of the target to be measured in the foregoing embodiment.
  • filtering at least two distance measurement values based on a preset error threshold to obtain the target distance measurement value includes: determining the distance measurement error value between the initial positioning estimate of the target to be measured and the target base station; The error value determines the corresponding cumulative distribution function; the corresponding preset error threshold is determined according to the cumulative distribution function; at least two distance measurement values are screened according to the preset error threshold to obtain the target distance measurement value.
  • filtering the distance measurement value between the target base station and the target to be measured based on the preset error threshold to optimize the Taylor positioning includes the following steps:
  • the measured value 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, the error needs to be particularly large before starting the Taylor algorithm Threshold filtering is performed on the data.
  • FIG. 2 is a schematic diagram of displaying a theoretical distance measurement value range provided by an embodiment of the present application.
  • a and B are the positions of the base station, and T is the true position of the target to be measured, where e is the expected measurement error, and the equation of the circle is:
  • the distance measurement value of A and B is between the radius of the great circle and the radius of the small circle. Since an initial value is obtained according to the improved Chan algorithm of simulated annealing before, the initial value is brought in, and the distance of each base station is calculated. Error, and calculate the cumulative distribution function. For example, it is possible to remove errors with an error of more than 90%, which can be exchanged for a part of the performance improvement, and part of the data can be filtered out.
  • the original Taylor algorithm uses the distance relationship between the target to be measured and the base station to calculate, namely:
  • R i,j represents the measured value of the distance between the target to be measured and the known base station.
  • all the position information can be used, and the measured value of the distance between the target to be measured can be added to establish an equation set.
  • (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 value of the distance between the target to be measured
  • R i ,j represents the measured value of the distance between the target to be measured and the known base station.
  • determining the position of the target to be measured according to the Taylor series algorithm of the multi-target source, the target distance measurement value, and the initial positioning estimation value includes: measuring the error value of the distance between the two targets to be measured, and The distance measurement error value between the target and the target base station forms the first matrix; the difference between the initial positioning estimation value of the target to be measured and the estimated coordinate value is used to form the second matrix; the distance between the target to be measured and the target base station is used The estimated value and the last estimated distance between the two targets to be tested form a third matrix; based on the preset positioning model, the corresponding fourth matrix is determined according to the first matrix, the second matrix and the third matrix; based on the minimum weight
  • the second matrix is calculated recursively on the second matrix by the square method, the fourth matrix, the third matrix and the preset covariance matrix, until the change between the estimated coordinate value of the target side to be measured and the initial positioning estimate is less than the preset threshold;
  • the initial positioning estimation value corresponding to less than the preset threshold value is used as
  • the Taylor series improvement algorithm with multiple target sources is introduced, and its characteristics include:
  • the initial value of the target to be tested (That is, the initial positioning estimate in the above-mentioned embodiment, which is the initial positioning estimate of multiple targets (1,2...M) at this time) Perform Taylor series expansion to remove the second-order or higher components, and the following equations are obtained Group:
  • R i,j is the estimated distance between the target to be measured and the known base station
  • e i,j is the distance measurement error between the target to be measured
  • e'i ,j is the distance measurement error between the target to be measured and the known base station.
  • Q represents the covariance matrix of the TDOA measurement value.
  • the value of (x i , y i ) is the final estimated position.
  • the value of (x i , y i ) is the target position to be measured in the above embodiment.
  • FIG. 3 is a flowchart of another collaborative determination method provided in an embodiment of the present application. As shown in Figure 3, this embodiment includes: S210-S260.
  • multiple TDOA measurement values between the target to be tested and the target base station are determined.
  • the initial estimated value of the target to be measured (that is, the initial coordinate estimated value in the foregoing embodiment) is obtained based on the simulated annealing algorithm.
  • the Chan algorithm which brings the initial estimated value into a close range, can determine the initial positioning estimate of the target to be measured.
  • the preset error threshold is used to filter at least two distance measurement values to obtain the target distance measurement value, that is, the error data equation refers to the distance measurement value with larger error.
  • the final result that is, the target position to be measured.
  • the target position to be measured is obtained, the target position to be measured is output and displayed for reference by the user.
  • the simulation steps include step 1 to step 10.
  • Step 1 For each unknown target i, the objective function of simulated annealing is defined as:
  • Step 2 Perform the following operations for each unknown target i to be tested:
  • Step 3 Use the 20 initial values obtained by the simulated annealing algorithm to calculate the matrix B in the Chan algorithm, bring it into formula (3), and find the first least square solution according to formula (5) That is, (x 0,i ,y 0,i ,R 0,i ) is obtained.
  • 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 to achieve higher positioning accuracy. Using the first estimate, construct a set of error equations for the second estimate.
  • Z 1, i represents a Z a
  • i of the first component e i Z a represents the estimation error.
  • (X 1 , Y 1 ) represents the known coordinates of the base station 1.
  • B′ i diag(x 0,i -X 1 ,y 0,i -Y 1 ,R 0,i ),
  • Step 5 get the Chan algorithm estimated position of 20 targets to be measured
  • Step 7 establish a system of equations:
  • Step 8 The estimated position obtained by the previous Chan algorithm Expanded and sorted out:
  • Step 9 Using Weighted Least Squares (WLS), an estimate of ⁇ can be obtained:
  • Q represents the covariance matrix of the TDOA measurement value.
  • Step 10 get the final estimation results (x 1 ,y 1 ),...,(x 20 ,y 20 ).
  • Fig. 4 is an error analysis diagram of a different algorithm provided by an embodiment of the present application. As shown in Figure 4, the improved Chan algorithm and Taylor series algorithm based on the simulated annealing algorithm have the smallest measurement error.
  • FIG. 5 is a schematic diagram of a comparison of positioning errors of different algorithms provided by an embodiment of the present application. As shown in Figure 5, the improved Chan algorithm and Taylor series algorithm based on the simulated annealing algorithm have the smallest positioning error.
  • FIG. 6 is a diagram of the relationship between a cumulative distribution and a measurement error method provided by an embodiment of the present application. As shown in Figure 6, the cumulative distribution and measurement error variance obtained by the improved Chan algorithm and Taylor series algorithm based on the simulated annealing algorithm are the smallest.
  • FIG. 7 is a schematic diagram of a positioning point distribution provided by an embodiment of the present application. As shown in Figure 7, the estimated positioning points obtained are concentrated near the true position of the target to be measured.
  • FIG. 8 is a structural block diagram of a cooperative positioning device provided by an embodiment of the present application.
  • the co-location device in this embodiment includes: a first determining module 310, a second determining module 320, and a third determining module 330.
  • the first determining module 310 is configured to use the simulated annealing algorithm and the first preset positioning algorithm to determine the initial positioning estimate of the target to be measured; the second determining module 320 is configured to filter at least two distance measurement values based on the preset error threshold, Obtain the target distance measurement value; at least two distance measurement values are the distance obtained by at least two measurements of the target to be measured and the target base station; the third determining module 330 is configured to measure the target distance according to the Taylor series algorithm of the multi-target source And the initial positioning estimate to determine the location of the target to be measured.
  • the co-location device provided in this embodiment is configured to implement the co-location method of the embodiment shown in FIG.
  • the first determining module 310 includes:
  • the first determining unit is configured to determine the initial coordinate estimation value of the target to be measured according to the simulated annealing algorithm; the second determining unit is configured to determine the initial positioning estimation value of the target to be measured based on the first preset positioning algorithm and the initial coordinate estimation value.
  • the first determining unit includes:
  • the first determining subunit is configured to calculate a preset target function according to the randomly generated initial coordinate value and the distance measurement value, where the distance measurement value is the distance obtained by measuring the target to be measured and the target base station; the second determining subunit is configured to determine The two randomly generated initial coordinate values respectively correspond to the incremental value between the two preset objective functions; the third determining sub-unit is configured to meet the preset criterion when the incremental value meets the preset criterion, and the current iteration number reaches the preset iteration number Threshold, and when the current temperature in the simulated annealing algorithm reaches the termination temperature, the latest randomly generated initial coordinate value is used as the initial coordinate estimate of the target to be measured.
  • the preset criterion includes one of the following:
  • the second determining unit includes:
  • the fourth determining subunit is configured to calculate the first preset diagonal matrix in the first preset positioning algorithm according to the initial coordinate estimation value, where the first preset diagonal matrix is the true distance between each target base station and the target to be measured
  • the fifth determining subunit is configured to calculate the corresponding first estimated value according to the first preset diagonal matrix and the preset noise vector covariance matrix
  • the sixth determining subunit is configured to obtain the corresponding first estimated value according to the first The estimated value and the preset estimation error obtain the second estimated value
  • the seventh determining subunit is configured to determine the initial value of the target to be measured according to the second estimated value, the second preset diagonal matrix and the known coordinate values of the target base station
  • the second preset diagonal matrix is a matrix formed according to the coordinate value of the target to be measured, the coordinate value of the target base station, and the estimated value of the distance between the target to be measured and the target base station.
  • the second determining module 320 includes:
  • the third determining unit is configured to determine the distance measurement error value between the initial positioning estimation value of the target to be measured and the target base station; the fourth determining unit is configured to determine the corresponding cumulative distribution function according to the distance measurement error value; the fifth determining unit , Configured to determine the corresponding preset error threshold according to the cumulative distribution function; the sixth determining unit is configured to filter at least two distance measurement values according to the preset error threshold to obtain the target distance measurement value.
  • the third determining module 330 includes:
  • the seventh determining unit is configured to compose the distance measurement error value between the two targets to be tested and the distance measurement error value between the target to be tested and the target base station into a first matrix; the eighth determining unit is configured to use the The difference between the initial positioning estimate of the target and the estimated coordinate value forms a second matrix; the ninth determining unit is configured to use the estimated distance between the target to be measured and the target base station, and the difference between the two targets to be measured The last distance estimate constitutes the third matrix; the tenth determining unit is configured to determine the corresponding fourth matrix based on the preset positioning model and according to the first matrix, the second matrix and the third matrix; the calculation unit is configured to be based on the minimum weight The second matrix is calculated recursively by the square method, the fourth matrix, the third matrix, and the preset covariance matrix, until the change between the estimated coordinate value of the target side to be measured and the initial positioning estimate is less than the preset threshold; The eleventh determining unit is configured to use an initial positioning estimation value corresponding to a value smaller than the
  • the first preset positioning algorithm is the Chan algorithm.
  • FIG. 9 is a schematic structural diagram of a device provided by an embodiment of the present application.
  • the device provided by the present application includes: a processor 410 and a memory 420.
  • the number of processors 410 in the device may be one or more.
  • One processor 410 is taken as an example in FIG. 9.
  • the number of memories 420 in the device may be one or more, and one memory 420 is taken as an example in FIG. 9.
  • the processor 410 and the memory 420 of the device may be connected through a bus or in other ways. In FIG. 9, the connection through a bus is taken as an example.
  • the device is a computer device.
  • the memory 420 can be configured to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the device of any embodiment of the present application (for example, the first determination in the co-location apparatus). Module 310, second determination module 320, and third determination module 330).
  • the memory 420 may include a program storage area and a data storage area.
  • the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the device, and the like.
  • the memory 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
  • the memory 420 may include a memory remotely provided with respect to the processor 410, and these remote memories may be connected to the device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
  • the above-provided device can be configured to execute the coordinated positioning method provided in any of the above-mentioned embodiments, and has corresponding functions and effects.
  • the embodiment of the present application also provides a storage medium containing computer-executable instructions.
  • the computer-executable instructions When executed by a computer processor, they are used to perform a co-location method.
  • the method includes: using a simulated annealing algorithm and a first preset The positioning algorithm determines the initial positioning estimate of the target to be measured; at least two distance measurement values are screened based on the preset error threshold to obtain the target distance measurement value; the at least two distance measurement values are performed at least twice between the target to be measured and the target base station The measured distance; the location of the target to be measured is determined according to the Taylor series algorithm of the multi-target source, the target distance measurement value and the initial positioning estimate value.
  • user equipment encompasses any suitable type of wireless user equipment, such as mobile phones, portable data processing devices, portable web browsers, or vehicular mobile stations.
  • the various embodiments of the present application can be implemented in hardware or dedicated circuits, software, logic or any combination thereof.
  • some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device, although the present application is not limited thereto.
  • Computer program instructions can be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or written in any combination of one or more programming languages Source code or object code.
  • ISA Instruction Set Architecture
  • the block diagram of any logic flow in the drawings of the present 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.
  • the computer program can be stored on the memory.
  • the memory can be of any type suitable for the local technical environment and can be implemented using any suitable data storage technology, such as but not limited to read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), optical Memory devices and systems (Digital Video Disc (DVD) or Compact Disk (CD)), etc.
  • Computer-readable media may include non-transitory storage media.
  • the data processor can be any type suitable for the local technical environment, such as but not limited to general-purpose computers, special-purpose computers, microprocessors, digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (ASICs) ), programmable logic devices (Field-Programmable Gate Array, FPGA), and processors based on multi-core processor architecture.
  • DSP Digital Signal Processing
  • ASICs application specific integrated circuits
  • FPGA Field-Programmable Gate Array
  • FPGA Field-Programmable Gate Array

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Abstract

一种协同定位方法、装置、设备和存储介质。协同定位方法包括：采用模拟退火算法和第一预设定位算法确定多个待测目标中每个待测目标的初始定位估计值；基于预设误差阈值对至少两个距离测量值进行筛选，得到目标距离测量值；根据多目标源的泰勒级数算法、每个目标距离测量值和每个初始定位估计值确定每个待测目标的位置。还包括与协同定位方法对应的协同定位装置，及实现协同定位方法的设备、存储实现协同定位算法程序的存储介质。

Description

协同定位方法、装置、设备和存储介质 技术领域
本申请涉及定位领域，例如涉及一种协同定位方法、装置、设备和存储介质。
背景技术
随着全球定位系统(Global Position System，GPS)的出现，定位需求在日常生活中开始变得越来越重要。在传统的定位算法中，泰勒(Taylor)级数展开法是解非线性方程组的最佳解法之一，但Taylor算法的缺点有两点，第一是对初始值较敏感，迭代的初始值对Taylor算法的影响较大，第二是可能会出现不收敛的情况。
发明内容
本申请提供一种协同定位方法、装置、设备和存储介质，实现了对待测目标的高精准定位。
本申请实施例提供一种协同定位方法，包括：
采用模拟退火算法和第一预设定位算法确定待测目标的初始定位估计值；基于预设误差阈值筛选至少两个距离测量值，得到目标距离测量值；所述至少两个距离测量值为对待测目标与目标基站进行至少两次测量得到的距离；根据多目标源的泰勒级数算法、所述目标距离测量值和所述初始定位估计值确定待测目标位置。
本申请实施例还提供一种协同定位装置，包括：
第一确定模块，配置为采用模拟退火算法和第一预设定位算法确定待测目标的初始定位估计值；第二确定模块，配置为基于预设误差阈值筛选至少两个距离测量值，得到目标距离测量值；所述至少两个距离测量值为对待测目标与目标基站进行至少两次测量得到的距离；第三确定模块，配置为根据多目标源的泰勒级数算法、所述目标距离测量值和所述初始定位估计值确定待测目标位置。
本申请实施例还提供一种设备，包括：存储器，以及一个或多个处理器；所述存储器，配置为存储一个或多个程序；当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现上述的协同定位方法。
本申请实施例还提供一种存储介质，所述存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述的协同定位方法。
附图说明
图1是本申请实施例提供的一种协同定位方法的流程图；
图2是本申请实施例提供的一种理论距离测量值范围的显示示意图；
图3是本申请实施例提供的另一种协同定法方法的流程图；
图4是本申请实施例提供的一种不同算法误差分析图；
图5是本申请实施例提供的一种不同算法定位误差对比示意图；
图6是本申请实施例提供的一种累计分布与测量误差方法的关系图；
图7是本申请实施例提供的一种定位点分布示意图；
图8是本申请实施例提供的一种协同定位装置的结构框图；
图9是本申请实施例提供的一种设备的结构示意图。
具体实施方式
下文中将结合附图对本申请的实施例进行说明。
在传统的定位算法中，Taylor级数展开法是解非线性方程组的最佳解法之一，Taylor级数展开法有着较高的求解精度和较快的迭代速度，使其成为最常用的定位算法之一。Taylor算法的缺点有两点，第一是对初始值较敏感，迭代的初始值对Taylor算法的影响较大，第二是可能会出现不收敛的情况。解决方法是利用多种算法进行协同定位。先用一种算法得出定位初始值，再使用该初始值带入Taylor级数展开法得到精确解。
到达时间差(Time Difference of Arrival，TDOA)定位算法模型，在时延估计得到多个TDOA测量值之后，可以建立定位方程组：
Figure PCTCN2021078794-appb-000001
在初始值求解方面，一般采用Chan算法获取定位初始值。Chan算法在测量误差服从高斯分布时，该算法定位精确，并且算法复杂度不高。Chan算法采用的两步加权最小二乘(Weighted Least Squares，WLS)，先假设变量是相互独立的，求得他们的估计值，再考虑他们之间的相互关系，求得到目标位置。
Figure PCTCN2021078794-appb-000002
其中，x,y和R分别是待测目标的坐标和与基站之间的距离的估计值。
定义误差向量ψ＝h-G aZ a，则：
φ＝E[ψψ T]≈c 2BQB        (3)
其中，第一对角矩阵B＝diag{r 1,r 2,…,r N}，r 1,r 2,…,r N是基站i与待测目标的真实距离，
Figure PCTCN2021078794-appb-000003
是服从高斯分布的噪声矢量协方差矩阵，假定Z a中的各个量相互独立，用加权最小二乘得到：
Figure PCTCN2021078794-appb-000004
由于B中有移动台(Mobile Station，MS)和基站探测器之间的距离，则φ是个未知量，还需要计算。
在待测目标与基站距离很远的情况下，可用Q代替，上述公式可以近似为：
Figure PCTCN2021078794-appb-000005
在待测目标距离基站距离较近的情况下，先假设待测目标距离基站很远，然后利用上述公式得到一个初始的粗略的解，利用该初始解可以计算B矩阵，然后再计算第一次和第二次WLS的结果。
Chan算法的假设是基于测量误差为零均值高斯分布，对于实际环境中误差较大的测量值，比如在有非视距(Non Line Of Sight，NLOS)误差的环境下，该算法的性能会下降。
在Taylor定位求解方面，定位精度受距离测量误差以及观测方程数量影响。距离测量误差越小、观测方程越多，定位效果就越好。可以通过一定手段消除误差较大的数据。同时定位算法一般都是建立终端和基站距离测量的观测方程，在基站数不多的情况下，方程数量受限，定位效果一般。有鉴于此，本申请实施例提出一种协同定位方法，根据模拟退火算法的改进Chan算法和泰勒级数算法，对待测目标进行高精度的定位。
在一实施例中，图1是本申请实施例提供的一种协同定位方法的流程图。本实施例适用于采用至少两种算法对待测目标进行协同定位的情况。本实施例中的协同定位方法包括S110-S130。
S110、采用模拟退火算法和第一预设定位算法确定待测目标的初始定位估计值。
在实施例中，第一预设定位算法为Chan算法。Chan算法是一种基于TDOA技术、具有解析表达式解的定位算法，在TDOA误差服从理想高斯分布时性能良好。在实施例中，待测目标指的是待测终端，比如，待测终端可以为待定位的用户设备(User Equipment，UE)。在实施例中，采用模拟退火算法和Chan算法协同确定待测目标的初始定位估计值，以便于得到待测目标准确的定位位置。模拟退火算法具有局部搜索能力强，运行时间较短的优点。在待测目标与每个基站之间的距离较近的情况下，第一次估算也需要一个估计初始值，才能求解初始解的估计矩阵。在实际生活中，比如，室内定位的场景下，待测目标与每个基站之间的距离较近，此时需要一个估计初始值(即本实施例中的初始定位估计值)。为此，在本申请实施例中，将模拟退火算法引入待测目标的初始定位估计值的求解过程中，是为了辅助Chan算法进行初始定位估计，即得到初始定位估计值。
S120、基于预设误差阈值筛选至少两个距离测量值，得到目标距离测量值。
在实施例中，至少两个距离测量值为对待测目标与目标基站进行至少两次测量得到的距离。在实施例中，可对待测目标和目标基站之间进行多次测量，以得到多个距离测量值，但在实际测量过程中，出现误差较大的距离测量值，为了实现对待测目标的准确测量，可配置一个预设误差阈值，对距离测量值进行筛除，从而得到较为准确的目标距离测量值。目标距离测量值可以为一个，也可以为多个，与所配置的预设误差阈值以及用户对待测目标的测量准确性高低有关，即在用户对待测目标的测量准确性高的情况下，对预设误差阈值配置高一些；反之，对预设误差阈值配置低一些。在实施例中，目标基站的坐标值为真实坐标值；而待测目标的坐标值为初始定位估计值。
在实施例中，根据目标基站的坐标值和待测目标的坐标值，可计算得到对应的距离估计值，将距离估计值和多次测量得到的距离测量值进行比较，并根据比较结果和预设误差阈值对距离测量值进行筛选，可得到较为准确的目标距离测量值。
S130、根据多目标源的泰勒级数算法、目标距离测量值和初始定位估计值确定待测目标位置。
在实施例中，多目标源的泰勒级数算法，指的是将多个待测目标之间的距离测量值参与计算的泰勒级数算法。在实施例中，基于多目标源的泰勒级数算法和Chan算法进行协同定义，可以有效的估计出待测目标的位置，并在误差不服从零均值的高斯分布的情况下，比常用算法的精度更高，更有效。
在一实施例中，采用模拟退火算法和第一预设定位算法确定待测目标的初始定位估计值，包括：
根据模拟退火算法确定待测目标的初始坐标估计值；基于第一预设定位算法和初始坐标估计值确定待测目标的初始定位估计值。
在一实施例中，根据模拟退火算法确定待测目标的初始坐标估计值，包括：
根据随机生成的初始坐标值与距离测量值计算预设目标函数，距离测量值为对待测目标与目标基站进行测量得到的距离；确定随机生成的两个初始坐标值分别对应的两个预设目标函数之间的增量值；在增量值满足预设准则，且当前迭代次数达到预设迭代次数阈值，以及模拟退火算法中的当前温度达到终止温度的情况下，将最新随机生成的初始坐标值作为待测目标的初始坐标估计值。
在一实施例中，预设准则，包括下述之一：
在增量值小于或等于零的情况下，接受随机生成的最新初始坐标值，并降低当前温度；在增量值大于零的情况下，以第一预设概率接受随机生成的最新初始坐标值。
在一实施例中，基于第一预设定位算法和初始坐标估计值确定待测目标的初始定位估计值，包括：
根据初始坐标估计值计算第一预设定位算法中的第一预设对角矩阵，第一预设对角矩阵为每个目标基站与待测目标之间真实距离组成的矩阵；根据第一预设对角矩阵和预设噪声矢量协方差矩阵计算得到对应的第一次估计值；根据第一次估计值和预设估计误差得到第二次估计值；根据第二次估计值、第二预设对角矩阵和目标基站的已知坐标值确定待测目标的初始定位估计值，第二预设对角矩阵为根据待测目标的坐标值、目标基站的坐标值，以及待测目标与目标基站之间的距离估计值组成的矩阵。
在实施例中，基于模拟退火算法的改进Chan算法获得初始解(即上述实施例中的初始定位估计值)的实施步骤，包含：
假设场所内共有N个基站，对于每个待测目标，模拟退火算法的预设目标函数设置为：
Figure PCTCN2021078794-appb-000006
其中，R i为待测目标与目标基站(已知坐标值的基站)的距离估计值，R i'为待测目标与目标基站的距离测量值。预设目标函数的含义为，使用估计的待测目标坐标求出的R i与距离测量值R' i之间差距的绝对值越小，则估计的坐标越准确。
在实施例中，基于模拟退火算法的改进Chan算法步骤如下：
步骤1，随机生成初始解ω，并计算预设目标函数J ω，当前迭代次数k＝0， 当前温度t 0＝t max，r∈(0,1)用来控制降温退火。在实施例中，初始解即为上述实施例中随机生成的初始坐标值。
步骤2，扰动产生新解ω'，并计算预设目标函数J ω'。
步骤3，计算增量值ΔJ＝J ω'-J ω。
步骤4，如果ΔJ≤0，则接受新解ω＝ω'，J ω＝J ω'，k＝k+1，降低温度t k＝rt k-1，否则按照Metropolis准则接受新解，即以第一预设概率(比如，
Figure PCTCN2021078794-appb-000007
)接受新解。
步骤5，判断是否达到预设迭代次数阈值，若未达到预设迭代次数阈值，继续步骤2。
步骤6，判断是否满足终止条件，终止条件为达到终止温度，若满足终止条件，则输出最终结果；不满足终止条件，则重置迭代次数k＝0，并降低初始温度t 0＝rt max。
步骤7，得到坐标估计初始值(x',y')。
步骤8，利用初始值计算Chan算法中的第一预设对角矩阵B，再带入公式(3)求出φ，之后利用公式(4)求出第一次最小二乘解
Figure PCTCN2021078794-appb-000008
即得到(x 0,y 0,R 0)。
步骤9，由于第一次最小二乘时未考虑x，y和R之间的关系，第二次最小二乘中考虑这三者之间的关系，从而实现更高的定位精度。利用第一次估计值，构造一组误差方程组进行第二次估计。
Figure PCTCN2021078794-appb-000009
其中，Z i表示Z a中的第i分量，e i表示Z a的估计误差。
定义新的误差向量：
ψ′＝h′-G′z′          (8)
其中：
Figure PCTCN2021078794-appb-000010
其中，(X 1,Y 1)代表基站1的已知坐标。
则ψ′的协方差矩阵为：
Figure PCTCN2021078794-appb-000011
其中，第二预设对角矩阵为：B′＝diag(x 0-X 1,y 0-Y 1,R 0)，
Figure PCTCN2021078794-appb-000012
同样采用之前的方法估计，得到：
Figure PCTCN2021078794-appb-000013
步骤10，得到最终估计位置
Figure PCTCN2021078794-appb-000014
在实施例中，最终估计位置Z即为上述实施例中待测目标的初始定位估计值。
在一实施例中，基于预设误差阈值筛选至少两个距离测量值，得到目标距离测量值，包括：确定待测目标的初始定位估计值与目标基站之间的距离测量误差值；根据距离测量误差值确定对应的累积分布函数；根据累计分布函数确定对应的预设误差阈值；根据预设误差阈值筛选至少两个距离测量值，得到目标距离测量值。
在实施例中，基于预设误差阈值筛选目标基站和待测目标间的距离测量值，优化泰勒定位，包含如下步骤：
由于测量值可能会有NLOS或者多径带来的延时误差，并且泰勒(Taylor)级数展开算法对初始值敏感，所以在得到初始的估计值之后，开始Taylor算法的之前需要将误差特别大的数据进行阈值筛选。
图2是本申请实施例提供的一种理论距离测量值范围的显示示意图。如图2所示，A，B为基站位置，T为待测目标的真实位置，其中，e为测量误差的期望，圆的方程为：
Figure PCTCN2021078794-appb-000015
Figure PCTCN2021078794-appb-000016
理论上，A，B的距离测量值在大圆半径与小圆半径之间，由于之前根据模拟退火的改进Chan算法得到了一个初始值，则带入初始值，计算每个基站距离该初始值的误差，并计算累积分布函数。比如，可以将90％误差以上的误差去除，既可以换来一部分的性能提升，并且可以筛除一部分数据。
设场所内共有N个基站，M个待测目标。由于传统的Taylor级数展开算法并未将待测目标之间的测量距离值考虑在内，损失了一部分的有用信息，从而导致定位的精度下降。
原本的Taylor算法采用待测目标与基站之间的距离关系进行计算，即：
Figure PCTCN2021078794-appb-000017
其中，R i,j表示待测目标与已知基站之间的距离测量值，为了让定位更精确，可以将所有位置信息都利用起来，加入待测目标之间的距离测量值建立方程组。
Figure PCTCN2021078794-appb-000018
其中，(x i,y i)表示待测目标的坐标值，(X i,Y i)表示已知基站的坐标值，R′ i,j表示待测目标之间的距离测量值，R i,j表示待测目标与已知基站之间的距离测量值。
在一实施例中，根据多目标源的泰勒级数算法、目标距离测量值和初始定位估计值确定待测目标位置，包括：将两个待测目标之间的距离测量误差值，以及待测目标与目标基站之间的距离测量误差值组成第一矩阵；利用待测目标的初始定位估计值和估计坐标值之间的差值组成第二矩阵；利用待测目标与目标基站之间的距离估计值，以及两个待测目标之间的上一次距离估计值组成第三矩阵；基于预设定位模型并根据第一矩阵、第二矩阵和第三矩阵确定对应的第四矩阵；基于加权最小二乘法、第四矩阵、第三矩阵和预设协方差矩阵对第二矩阵进行递归计算，直至待测目标侧估计坐标值与初始定位估计值之间的变化量小于预设门限值；将小于预设门限值对应的初始定位估计值作为待测目标位置。
在实施例中，获得初始解后带入多目标源的Taylor级数改进算法，其特征包含：
在待测目标的初始值
Figure PCTCN2021078794-appb-000019
(即上述实施例中的初始定位估计值，此时为多个待测目标(1,2……M)的初始定位估计值)处进行泰勒级数展开，去除二阶以上分量，得到下列方程组：
Figure PCTCN2021078794-appb-000020
其中，
Figure PCTCN2021078794-appb-000021
为待测目标之间的上一次距离估计值，R i，j为待测目标与已知基站之间的距离估计值，
Figure PCTCN2021078794-appb-000022
e i,j为待测目标之间距离测量误差，e′ i,j为待测目标与已知基站之间的距离测量误差。
整理后得到定位模型：
h＝GΔ+E          (14)
其中，
Figure PCTCN2021078794-appb-000023
Figure PCTCN2021078794-appb-000024
对式(14)使用加权最小二乘法(WLS)，可以得到对Δ的估计：
Δ＝(G TQ -1G) -1G TQ -1h        (15)
其中，Q代表TDOA测量值的协方差矩阵。在第二次递归计算中，令
Figure PCTCN2021078794-appb-000025
重复计算多次，直到Δx i和Δy i都足够小，满足一个设定的门限值ε：
Figure PCTCN2021078794-appb-000026
此时，(x i,y i)的值即为最终的估计位置。在实施例中，(x i,y i)的值即为上述实施例中待测目标位置。
在一实施例中，图3是本申请实施例提供的另一种协同定法方法的流程图。如图3所示，本实施例包括：S210-S260。
S210、确定TDOA测量值。
在实施例中，确定待测目标与目标基站之间的多个TDOA测量值。
S220、利用模拟退火算法获得初始估计值。
在实施例中，基于模拟退火算法获取得到待测目标的初始估计值(即上述实施例中的初始坐标估计值)。
S230、带入近距离的Chan算法，得到初始定位估计值。
在实施例中，将初始估计值带入近距离的Chan算法，可确定待测目标的初始定位估计值。
S240、将错误数据方程去除。
在实施例中，利用预设误差阈值筛选至少两个距离测量值，以得到目标距离测量值，即错误数据方程指的是误差较大的距离测量值。
S250、将初始定位估计值带入多目标泰勒算法。
在实施例中，基于多目标泰勒算法、初始定位估计值和目标距离测量值，可得到最终结果，即待测目标位置。
S260、输出最终结果。
在实施例中，在得到待测目标位置之后，将待测目标位置输出并显示，以供用户进行参考。
在一实现方式中，在100m×100m的平面内随机放置20个未知位置的待测目标，5个已知位置的基站。假设距离测量误差服从10m，方差为δ 2＝1的指数分布。仿真步骤包括步骤1-步骤10。
步骤1，对每一个未知待测目标i，模拟退火的目标函数定义为：
Figure PCTCN2021078794-appb-000027
步骤2，对每一个未知待测目标i，进行如下操作：
1)设置迭代终止次数为100，温度下降参数r＝0.98，初始温度t max＝100。
2)扰动产生新解ω′ i，并计算目标函数
Figure PCTCN2021078794-appb-000028
3)计算增量
Figure PCTCN2021078794-appb-000029
4)如果ΔJ≤0，则接受新解ω i＝ω′ i,
Figure PCTCN2021078794-appb-000030
k＝k+1，降低温度t k＝rt k-1，否则按照Metropolis准则接受新解，即以概率
Figure PCTCN2021078794-appb-000031
接受新解。
5)判断是否满足终止条件，终止条件为达到终止温度，若满足终止条件则输出最终结果，若不满足终止条件则重置迭代次数k＝0，并降低初始温度t 0＝rt max。
6)得到坐标估计初始值(x′ i,y′ i)。
步骤3，利用模拟退火算法得到的20个初始值计算Chan算法中的矩阵B，带入公式(3)求出，根据公式(5)求出第一次最小二乘解
Figure PCTCN2021078794-appb-000032
即得到(x 0,i,y 0,i,R 0,i)。
步骤4，由于第一次最小二乘时没有考虑x，y和R之间的关系，第二次最小二乘中将会考虑，从而实现更高的定位精度。利用第一次估计值，构造一组误差方程组进行第二次估计。
Figure PCTCN2021078794-appb-000033
其中，Z 1,i表示Z a,i中的第1分量，e i表示Z a的估计误差。
定义新的误差向量：
ψ′ i＝h′ i-G′ iz′ i,i＝1,…,20
其中，
Figure PCTCN2021078794-appb-000034
其中，(X 1,Y 1)代表基站1的已知坐标。
则ψ′的协方差矩阵为：
Figure PCTCN2021078794-appb-000035
其中，B′ i＝diag(x 0,i-X 1,y 0,i-Y 1,R 0,i)，
Figure PCTCN2021078794-appb-000036
同样采用之前的方法估计，得到：
Figure PCTCN2021078794-appb-000037
步骤5，得到20个待测目标的Chan算法估计位置
Figure PCTCN2021078794-appb-000038
Figure PCTCN2021078794-appb-000039
步骤6，通过改进Chan算法得到的初始位置估计Z k,k＝1,…,20，分别计算每个基站坐标距离该初始值的累积分布函数，
Figure PCTCN2021078794-appb-000040
Figure PCTCN2021078794-appb-000041
将误差超过90％的函数去除。
步骤7，建立方程组：
Figure PCTCN2021078794-appb-000042
步骤8，在之前Chan算法得到的估计位置
Figure PCTCN2021078794-appb-000043
处展开，并整理得到：
Figure PCTCN2021078794-appb-000044
Figure PCTCN2021078794-appb-000045
步骤9，使用加权最小二乘法(WLS)，可以得到对Δ的估计：
Δ＝(G TQ -1G T) -1G TQ -1h
其中，Q代表TDOA测量值的协方差矩阵。在第二次递归计算中，令
Figure PCTCN2021078794-appb-000046
重复计算最多50次，直到Δx i和Δy i都足够小。
步骤10，得到最终估计结果(x 1,y 1),…,(x 20,y 20)。
图4是本申请实施例提供的一种不同算法误差分析图。如图4所示，基于模拟退火算法的改进Chan算法和泰勒级数算法得到的测量误差最小。
在其他不变的情况下，分析误差的方差与定位精度的关系。图5是本申请实施例提供的一种不同算法定位误差对比示意图。如图5所示，基于模拟退火算法的改进Chan算法和泰勒级数算法得到的定位误差最小。
在δ^2＝0.5时，重复测试50次测试定位误差分布函数和方差的关系。图6是本申请实施例提供的一种累计分布与测量误差方法的关系图。如图6所示，基于模拟退火算法的改进Chan算法和泰勒级数算法得到的累计分布与测量误差方差最小。
在真实目标在(60,65)点的情况下，运行20次算法，得到定位点分布。图7是本申请实施例提供的一种定位点分布示意图。如图7所示，所得到的估计定位点集中在待测目标的真实位置附近。
图8是本申请实施例提供的一种协同定位装置的结构框图。如图8所示，本实施例中的协同定位装置包括：第一确定模块310、第二确定模块320和第三确定模块330。
第一确定模块310，配置为采用模拟退火算法和第一预设定位算法确定待测目标的初始定位估计值；第二确定模块320，配置为基于预设误差阈值筛选至少两个距离测量值，得到目标距离测量值；至少两个距离测量值为对待测目标与目标基站进行至少两次测量得到的距离；第三确定模块330，配置为根据多目标源的泰勒级数算法、目标距离测量值和初始定位估计值确定待测目标位置。
本实施例提供的协同定位装置设置为实现图1所示实施例的协同定位方法，本实施例提供的协同定位装置实现原理和技术效果类似，此处不再赘述。
在一实施例中，第一确定模块310，包括：
第一确定单元，配置为根据模拟退火算法确定待测目标的初始坐标估计值；第二确定单元，配置为基于第一预设定位算法和初始坐标估计值确定待测目标的初始定位估计值。
在一实施例中，第一确定单元，包括：
第一确定子单元，配置为根据随机生成的初始坐标值与距离测量值计算预设目标函数，距离测量值为对待测目标与目标基站进行测量得到的距离；第二确定子单元，配置为确定随机生成的两个初始坐标值分别对应的两个预设目标函数之间的增量值；第三确定子单元，配置为在增量值满足预设准则，且当前迭代次数达到预设迭代次数阈值，以及模拟退火算法中的当前温度达到终止温度的情况下，将最新随机生成的初始坐标值作为待测目标的初始坐标估计值。
在一实施例中，预设准则，包括下述之一：
在增量值小于或等于零的情况下，接受随机生成的最新初始坐标值，并降低当前温度；在增量值大于零的情况下，以第一预设概率接受随机生成的最新初始坐标值。
在一实施例中，第二确定单元，包括：
第四确定子单元，配置为根据初始坐标估计值计算第一预设定位算法中的第一预设对角矩阵，第一预设对角矩阵为每个目标基站与待测目标之间真实距离组成的矩阵；第五确定子单元，配置为根据第一预设对角矩阵和预设噪声矢量协方差矩阵计算得到对应的第一次估计值；第六确定子单元，配置为根据第一次估计值和预设估计误差得到第二次估计值；第七确定子单元，配置为根据第二次估计值、第二预设对角矩阵和目标基站的已知坐标值确定待测目标的初始定位估计值，第二预设对角矩阵为根据待测目标的坐标值、目标基站的坐标值，以及待测目标与目标基站之间的距离估计值组成的矩阵。
在一实施例中，第二确定模块320，包括：
第三确定单元，配置为确定待测目标的初始定位估计值与目标基站之间的距离测量误差值；第四确定单元，配置为根据距离测量误差值确定对应的累积分布函数；第五确定单元，配置为根据累计分布函数确定对应的预设误差阈值；第六确定单元，配置为根据预设误差阈值筛除至少两个距离测量值，得到目标距离测量值。
在一实施例中，第三确定模块330，包括：
第七确定单元，配置为将两个待测目标之间的距离测量误差值，以及待测目标与目标基站之间的距离测量误差值组成第一矩阵；第八确定单元，配置为利用待测目标的初始定位估计值和估计坐标值之间的差值组成第二矩阵；第九 确定单元，配置为利用待测目标与目标基站之间的距离估计值，以及两个待测目标之间的上一次距离估计值组成第三矩阵；第十确定单元，配置为基于预设定位模型并根据第一矩阵、第二矩阵和第三矩阵确定对应的第四矩阵；计算单元，配置为基于加权最小二乘法、第四矩阵、第三矩阵和预设协方差矩阵对第二矩阵进行递归计算，直至待测目标侧估计坐标值与初始定位估计值之间的变化量小于预设门限值；第十一确定单元，配置为将小于预设门限值对应的初始定位估计值作为待测目标位置。
在一实施例中，第一预设定位算法为Chan算法。
图9是本申请实施例提供的一种设备的结构示意图。如图9所示，本申请提供的设备，包括：处理器410、存储器420。该设备中处理器410的数量可以是一个或者多个，图9中以一个处理器410为例。该设备中存储器420的数量可以是一个或者多个，图9中以一个存储器420为例。该设备的处理器410和存储器420可以通过总线或者其他方式连接，图9中以通过总线连接为例。在该实施例中，该设备为计算机设备。
存储器420作为一种计算机可读存储介质，可设置为存储软件程序、计算机可执行程序以及模块，如本申请任意实施例的设备对应的程序指令/模块(例如，协同定位装置中的第一确定模块310、第二确定模块320和第三确定模块330)。存储器420可包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据设备的使用所创建的数据等。此外，存储器420可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器420可包括相对于处理器410远程设置的存储器，这些远程存储器可以通过网络连接至设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
上述提供的设备可设置为执行上述任意实施例提供的协同定位方法，具备相应的功能和效果。
本申请实施例还提供一种包含计算机可执行指令的存储介质，计算机可执行指令在由计算机处理器执行时用于执行一种协同定位方法，该方法包括：采用模拟退火算法和第一预设定位算法确定待测目标的初始定位估计值；基于预设误差阈值筛选至少两个距离测量值，得到目标距离测量值；所述至少两个距离测量值为对待测目标与目标基站进行至少两次测量得到的距离；根据多目标源的泰勒级数算法、所述目标距离测量值和所述初始定位估计值确定待测目标位置。
术语用户设备涵盖任何适合类型的无线用户设备，例如移动电话、便携数 据处理装置、便携网络浏览器或车载移动台。
一般来说，本申请的多种实施例可以在硬件或专用电路、软件、逻辑或其任何组合中实现。例如，一些方面可以被实现在硬件中，而其它方面可以被实现在可以被控制器、微处理器或其它计算装置执行的固件或软件中，尽管本申请不限于此。
本申请的实施例可以通过移动装置的数据处理器执行计算机程序指令来实现，例如在处理器实体中，或者通过硬件，或者通过软件和硬件的组合。计算机程序指令可以是汇编指令、指令集架构(Instruction Set Architecture，ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码。
本申请附图中的任何逻辑流程的框图可以表示程序步骤，或者可以表示相互连接的逻辑电路、模块和功能，或者可以表示程序步骤与逻辑电路、模块和功能的组合。计算机程序可以存储在存储器上。存储器可以具有任何适合于本地技术环境的类型并且可以使用任何适合的数据存储技术实现，例如但不限于只读存储器(Read-Only Memory，ROM)、随机访问存储器(Random Access Memory，RAM)、光存储器装置和系统(数码多功能光碟(Digital Video Disc，DVD)或光盘(Compact Disk，CD))等。计算机可读介质可以包括非瞬时性存储介质。数据处理器可以是任何适合于本地技术环境的类型，例如但不限于通用计算机、专用计算机、微处理器、数字信号处理器(Digital Signal Processing，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程逻辑器件(Field-Programmable Gate Array，FPGA)以及基于多核处理器架构的处理器。

Claims (11)

  1. 一种协同定位方法，包括：
    采用模拟退火算法和第一预设定位算法确定多个待测目标中每个待测目标的初始定位估计值；
    基于预设误差阈值对至少两个距离测量值进行筛选，得到目标距离测量值；所述至少两个距离测量值为对每个待测目标与多个目标基站中每个目标基站之间的距离进行至少两次测量得到的测量值；
    根据多目标源的泰勒级数算法、每个目标距离测量值和每个初始定位估计值确定每个待测目标的位置。
  2. 根据权利要求1所述的方法，其中，所述采用模拟退火算法和第一预设定位算法确定多个待测目标中每个待测目标的初始定位估计值，包括：
    根据所述模拟退火算法确定每个待测目标的初始坐标估计值；
    基于所述第一预设定位算法和所述初始坐标估计值确定所述待测目标的初始定位估计值。
  3. 根据权利要求2所述的方法，其中，所述根据所述模拟退火算法确定每个待测目标的初始坐标估计值，包括：
    根据随机生成的初始坐标值与所述距离测量值计算预设目标函数；
    确定所述随机生成的两个初始坐标值分别对应的两个预设目标函数之间的增量值；
    在所述增量值满足预设准则，且当前迭代次数达到预设迭代次数阈值，以及所述模拟退火算法中的当前温度达到终止温度的情况下，将随机生成的最新初始坐标值作为所述待测目标的初始坐标估计值。
  4. 根据权利要求3所述的方法，其中，所述预设准则，包括下述之一：
    在所述增量值小于或等于零的情况下，接受所述随机生成的最新初始坐标值，并降低所述模拟退火算法中的当前温度；
    在所述增量值大于零的情况下，以第一预设概率接受所述随机生成的最新初始坐标值。
  5. 根据权利要求2所述的方法，其中，所述基于所述第一预设定位算法和所述初始坐标估计值确定每个待测目标的初始定位估计值，包括：
    根据所述初始坐标估计值计算所述第一预设定位算法中的第一预设对角矩阵，所述第一预设对角矩阵为每个目标基站与所述待测目标之间真实距离组成的矩阵；
    根据所述第一预设对角矩阵和预设噪声矢量协方差矩阵计算得到第一次估计值；
    根据所述第一次估计值和预设估计误差得到第二次估计值；
    根据所述第二次估计值、第二预设对角矩阵和一个目标基站的已知坐标值确定所述待测目标的初始定位估计值，所述第二预设对角矩阵为根据所述第一次估计值中所述待测目标的坐标值、所述一个目标基站的已知坐标值，以及所述第一次估计值中所述待测目标与每个目标基站之间的距离估计值组成的矩阵。
  6. 根据权利要求1所述的方法，其中，所述基于预设误差阈值对至少两个距离测量值进行筛选，得到目标距离测量值，包括：
    确定每个待测目标的初始定位估计值与每个目标基站之间的距离测量误差值；
    根据所述距离测量误差值确定累积分布函数；
    根据所述累计分布函数确定所述预设误差阈值；
    根据所述预设误差阈值对所述至少两个距离测量值进行筛选，得到所述目标距离测量值。
  7. 根据权利要求1所述的方法，其中，所述根据多目标源的泰勒级数算法、所述目标距离测量值和所述初始定位估计值确定所述待测目标的位置，包括：
    在所述待测目标的数量为两个的情况下，将两个待测目标之间的距离测量误差值，以及每个待测目标与每个目标基站之间的距离测量误差值组成第一矩阵；
    利用每个待测目标的初始定位估计值和估计坐标值之间的差值组成第二矩阵；
    利用每个待测目标与每个目标基站之间的目标距离测量值，以及所述两个待测目标之间的上一次距离估计值组成第三矩阵；
    基于预设定位模型并根据所述第一矩阵、所述第二矩阵和所述第三矩阵确定第四矩阵；
    基于加权最小二乘法、所述第四矩阵、所述第三矩阵和预设协方差矩阵对所述第二矩阵进行递归计算，直至每个待测目标的估计坐标值与初始定位估计值之间的变化量小于预设门限值；
    将小于所述预设门限值的所述变化量对应的初始定位估计值作为所述待测目标的位置。
  8. 根据权利要求1-7中任一项所述的方法，其中，所述第一预设定位算法为Chan算法。
  9. 一种协同定位装置，包括：
    第一确定模块，配置为采用模拟退火算法和第一预设定位算法确定多个待测目标中每个待测目标的初始定位估计值；
    第二确定模块，配置为基于预设误差阈值对至少两个距离测量值进行筛选，得到目标距离测量值；所述至少两个距离测量值为对每个待测目标与目标基站之间的距离进行至少两次测量得到的测量值；
    第三确定模块，配置为根据多目标源的泰勒级数算法、每个目标距离测量值和每个初始定位估计值确定每个待测目标的位置。
  10. 一种设备，包括：存储器，以及至少一个处理器；
    所述存储器，配置为存储至少一个程序；
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-8中任一项所述的协同定位方法。
  11. 一种存储介质，存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1-8中任一项所述的协同定位方法。
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