A Correlation Method for Adaptive Decision Making in Complex Environments Based on Error Propagation

Abstract

This invention discloses an adaptive decision-making association method based on the error propagation method in complex environments, belonging to the field of radar data association technology. Using error propagation theory, the invention accurately and adaptively calculates the multi-factor gates, confidence intervals, and membership degrees corresponding to point traces. Under uncertain target motion conditions, the association score is calculated based on the Dessignor-Sternberg evidence theory. The method utilizes a minimal number of parameters and is easy to configure. The error propagation method employed is computationally lightweight and easy to implement, making it of great practical engineering significance.

Description

Technical Field

[0001] The present invention relates to the technical field of radar data association, and in particular to an association method for adaptive decision-making based on an error propagation method in a complex environment. Background Art

[0002] In an increasingly complex electromagnetic environment, reliable and accurate multi-target correlation tracking is of great significance. The data association problem is the core of radar data processing, and the correct association of point tracks and tracks is a prerequisite for achieving stable target tracking. Traditional correlation methods are mostly based on statistical theory, such as the nearest neighbor method (NN), probabilistic data association method (PDA), joint probabilistic data association (JPDA), and multiple hypothesis tracking (MHT). The nearest neighbor method is prone to mistracking and loss of tracking in dense clutter conditions, while the traditional PDA, JPDA, and MHT algorithms will increase exponentially with the increase in the number of targets and echoes, and even produce a combinatorial explosion phenomenon, resulting in poor real-time performance of the algorithm and inability to correlate normally. The paper "Simplified JPDA Multi-Target Tracking Algorithm in Dense Clutter Environment" (Signal Processing, 2020, 36(8): 1280-1287) proposes a new correlation probability calculation method, which corrects the correlation probability of measurement and target by defining the filter participation, avoids matrix splitting, and improves the real-time performance of the algorithm. The paper "Fast Data Association Algorithm Based on Fuzzy Information Fusion" (Journal of Projectiles, Rockets and Guidance, 2011, 31(1): 201-203) aims at the problem of target tracking under dense clutter. Based on the PDA algorithm, it proposes an optimization algorithm for fuzzy fusion of target distance and azimuth features. However, this method does not solve the problem of exponential growth of computational complexity caused by the increase in the number of targets. In addition, there is the problem that fuzzy membership parameters and PDA parameters are difficult to select, which is not conducive to engineering applications.

[0003] In summary, existing point-to-head navigation association methods for dense clutter and complex target motion often suffer from exponentially increasing computational complexity and difficult parameter adjustment. This is particularly true when the target is maneuvering, which can easily lead to mistracking and loss of tracking. These issues urgently need to be addressed. To this end, a new association method based on error propagation and adaptive decision-making in complex environments is proposed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to solve the problems of exponential growth of computational complexity and difficulty in adjusting parameters in existing point-to-point navigation association methods, and provide an association method based on adaptive decision-making in complex environments based on the error propagation method.

[0005] The present invention solves the above technical problems through the following technical solutions, which include the following steps:

[0006] S1: Convert the polar coordinate data of the radar trace to the rectangular coordinate system, and obtain the variance of the trace in the rectangular coordinate system based on the variance of the polar coordinate system of the distance and azimuth;

[0007] S2: Use the IMM-UKF filtering algorithm to predict the multi-factor prediction information of the track time;

[0008] S3: Calculate the variance estimate of the multi-factor based on the trace data and the prediction information of the multi-factor;

[0009] S4: First, establish an azimuth-range adaptive sector wave gate, then use the 4σ criterion based on the variance estimate of multiple factors to establish a multi-factor adaptive multiple gate to effectively filter out false traces. The 4σ criterion is four times the standard deviation criterion.

[0010] S5: Based on the prediction information of the multiple factors in step S2 and the variance estimate of the multiple factors in step S3, the confidence interval of the multiple factors is established using the 3σ criterion, which is the 3 times standard deviation criterion;

[0011] S6: Based on the trace data in step S1, the prediction information of the multiple factors in step S2 and the confidence interval of the multiple factors in step S5, the membership degree of the multiple factors is calculated;

[0012] S7: Based on the membership of the multiple factors in step S6, the correlation scores of the adaptive multiple gate traces are calculated according to the DS evidence theory, and the trace with the highest correlation score is selected as the correlation trace.

[0013] Furthermore, in step S1, the polar coordinate data of the radar trace is converted to a rectangular coordinate system, and the conversion result is:

[0014] x k =ρ k cosθ k

[0015] y k =ρ k sinθ k

[0016] Among them, (ρ k ,θ k ) is the polar coordinate data of the point at time k, (x k ,y k ) is the rectangular coordinate system data of the point trace at time k;

[0017] According to the variance of the polar coordinate system where the distance and azimuth are located, the variance of the point trace in the rectangular coordinate system is obtained. The definition is as follows:

[0018]

[0019] in, are all parameters required for unbiased correction, are the distance and azimuth (ρ k ,θ k ) is the measurement error variance.

[0020] Furthermore, in step S2, the track time t k is the kth moment, t k+1 If the time is k+1, the IMM-UKF filtering algorithm is used to predict the track information to the point track time t k+1 time, get t k+1 The track prediction value at the moment, that is, the prediction information of multiple factors, is expressed as follows:

[0021] Speed ​​prediction value:

[0022] Heading prediction value:

[0023] Distance change rate:

[0024] Distance change rate prediction value:

[0025] Distance acceleration prediction value:

[0026] in, is the track position prediction value in the rectangular coordinate system, is the track position prediction value in the polar coordinate system,

[0027] Furthermore, in step S3, the variance estimates of the multiple factors are as follows:

[0028] Speed ​​variance:

[0029] Heading variance:

[0030] Distance acceleration variance:

[0031] in:

[0032] T is the radar scanning period;

[0033]

[0034] Furthermore, in step S4, the process of establishing the azimuth distance adaptive sector gate is as follows: As the center, select the points within the distance and direction confidence range (ρ k+1 ,θ k+1 ), determined as a preliminary candidate point trace, where the distance and direction confidence range form a fan-shaped gate.

[0035] Furthermore, in step S4, the adaptive multi-gates of the multiple factors are as follows:

[0036] Speed ​​gate: dot velocity V k+1 satisfy:

[0037] Heading gate: Point track heading C k+1 satisfy:

[0038] Distance acceleration gate: point distance acceleration satisfy:

[0039] in:

[0040]

[0041] When a point is within the range of the azimuth distance adaptive sector gate and also within the range of the adaptive multi-factor gate, it is determined as the final candidate point, thus achieving effective filtering of false points.

[0042] Furthermore, in step S5, the confidence intervals of the multiple factors are confidence intervals of speed, heading and distance acceleration, which are defined as:

[0043] Speed ​​factor V k+1 The confidence interval for is:

[0044] Heading factor C k+1 The confidence interval for is:

[0045] Distance acceleration factor The confidence interval for is:

[0046] Furthermore, in step S6, the membership degree calculation formula is as follows:

[0047]

[0048] Among them, s is the factor to be calculated, p(s) is the membership degree of s, and U is the upper bound of the factor error;

[0049] The factors to be calculated include:

[0050] Velocity V of the dot k+1 The speed prediction value of the track The absolute value of the difference:

[0051]

[0052] Course of point track C k+1 The heading prediction value of the track The absolute value of the difference:

[0053]

[0054] Distance acceleration of the point trace Acceleration prediction value of distance from track The absolute value of the difference:

[0055]

[0056] The upper bounds U of the errors corresponding to the factors to be calculated are and

[0057] Substitute the above factors to be calculated and the corresponding upper bound of the error into the membership calculation formula to calculate the membership p(V k+1 )、p(C k+1 )and

[0058] Furthermore, in step S7, the membership calculation results of the three factors of speed, heading and distance acceleration are fused according to the DS evidence theory to calculate the point trace association score, and the data is fused after processing according to the membership calculation results of the three factors. k+1 )、p(C k+1 )and First set the degree of membership as follows:

[0059]

[0060] The probability of judging a point as a related point is m V (p), m C (p), m a (p), whose uncertain probabilities are The specific formula for the probability of fusion judgment point trace being a related point is:

[0061]

[0062] The correlation scores of all candidate traces are calculated according to the above formula, and the trace with the largest correlation score is selected as the associated trace.

[0063] Compared with the prior art, the present invention has the following advantages:

[0064] (1) Based on probability theory, the multi-factor variance estimation is highly accurate. Based on the error propagation theory and the variance of the distance and azimuth of the trace, the variance of the multi-factor of the radar measurement trace can be accurately and adaptively calculated.

[0065] (2) Based on statistical theory, the risk of false tracking and loss of tracking is greatly reduced. The adaptive multi-gate calculator filters out most false points and ensures that 100% of the real target points fall into the gate. The multi-factor membership calculator accurately calculates the membership of the associated points, and the association score calculator accurately selects the correct associated points.

[0066] (3) Few parameters and easy to set. This method has good adaptability to the distance and orientation of different point traces and has very few parameters that are easy to set.

[0067] (4) Small amount of calculation, easy to implement in engineering. The error propagation method adopted in the present invention has the characteristics of small amount of calculation and easy to implement.

[0068] (5) The DS evidence theory has good adaptability. In complex environments, the target is a non-cooperative target and the motion state is completely uncertain, so the DS evidence theory has good adaptability when calculating the association score. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 1 is a flow chart of an association method for adaptive decision-making based on an error propagation method in a complex environment according to an embodiment of the present invention;

[0070] Figure 2 is the trajectory of the track target movement in the embodiment of the present invention;

[0071] Figure 3 is a curve showing the change of the covariance of the position information with distance in an embodiment of the present invention, where (a) is the X covariance and (b) is the Y covariance;

[0072] Figure 4 : The upper and lower bounds of the multi-factor error of the track target and the actual change curve in the embodiment of the present invention are shown, where (a) is the speed factor, (b) is the heading factor, and (c) is the range acceleration factor;

[0073] Figure 5 1 is a curve diagram of the change of the multi-factor error mean square error of the track target in an embodiment of the present invention, wherein (a) is the speed factor, (b) is the heading factor, and (c) is the distance acceleration factor. DETAILED DESCRIPTION

[0074] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0075] like Figure 1 As shown, this embodiment provides a technical solution: an association method for adaptive decision-making based on error propagation method in a complex environment, comprising the following steps:

[0076] In this embodiment, the data association of two-dimensional radar is considered, and the radar trace data at time k includes the trace time t k , distance ρ k 、Direction θ k The measurement error variances of distance and azimuth are and Track data includes track time t k , the rectangular coordinate position of the end point of the track Polar coordinate position speed course

[0077] Step (a): Use the coordinate conversion method to convert the polar coordinate system data of the point trace into the rectangular coordinate system. The conversion result is:

[0078] x k =ρ k cosθ k

[0079] y k =ρ k sinθ k

[0080] Among them, x k and y k They represent the position information of the point trace in the rectangular coordinate system at time k respectively;

[0081] Based on probability theory, the unbiased corrected covariance solution method is:

[0082]

[0083] in, These are all parameters required for unbiased correction;

[0084] It should be noted that the variance of the orientation in the polar coordinate system is And the variance of the distance in polar coordinates Substitute λ1 and λ2 into the covariance solution formula.

[0085] In this embodiment, the distance standard deviation σ is set ρ=20 (unit: meter), azimuth mean square error σ θ =0.15 (unit: degree). Figure 2 The trajectory of the target making complex maneuvers can be obtained according to the above formula: Figure 3 The position information change curve shown in the figure shows that the track target performs complex maneuvers. It can be seen that in the case of complex motion, its covariance is maintained within a certain range and complies with the error variance transfer rule of distance and azimuth.

[0086] Step (b): The track predictor can be implemented using a filter. In this embodiment, the IMM-UKF filter (Interactive Multiple Model-Unscented Kalman Filter) algorithm is used to address complex environments. This is a suboptimal algorithm for hybrid system state estimation. The main steps of the algorithm are as follows:

[0087] The first step is to initialize the model conditions and calculate the mixing probability

[0088] Assume that the number of sub-models in the IMM-UKF filter is r, the j-th model is valid at the current moment, and the filter input matched with it is the estimated μ of each filter at the previous moment. k Mixed.

[0089] Assume that the matching model at time k-1 is The matching model at time k is Measured information Z k-1 The mixing probability conditional on is:

[0090]

[0091] Among them, π ij is the assumed Markov model transition probability,

[0092] For j = 1, 2, ..., r, the initial state mixture estimate and the covariance matrix according to the mixed estimate They are:

[0093]

[0094]

[0095] The second step is to filter each sub-model separately

[0096] Given the initialized state and covariance matrix, we can obtain the new measurement Z k Afterwards, the state estimate is updated.

[0097] For each sub-model i=1,2,…,r, the state prediction is performed respectively:

[0098]

[0099] Update the measurement residuals and their covariance matrices of the sub-model:

[0100]

[0101] Simultaneous calculation and model Matching likelihood function (under Gauss assumption):

[0102]

[0103] The third step is to update the model probability:

[0104] For each sub-model i=1,2,…,r, calculate its filter gain matrix, state estimate update and state estimate update error covariance matrix respectively:

[0105]

[0106] Calculate the model probability respectively:

[0107]

[0108] in, Normalization constant

[0109] Step 4: State estimation fusion:

[0110] Compute the overall estimate at time k and the overall estimation error covariance matrix P k|k for:

[0111]

[0112]

[0113] The output of the filter is a weighted average of the multiple filter estimation results. In this embodiment, the results of the CV and Singer filter models are weighted.

[0114] The above is the prediction formula of the IMM-UKF filtering algorithm, taking the track time t k is the kth moment, t k+1 If the time is k+1, the IMM-UKF filtering algorithm can be used to predict the track information to the point track time t k+1 time, get t k+1 The track prediction values ​​at the moment, including the track position, speed and heading, are expressed as:

[0115] The state prediction fusion in the rectangular coordinate system is as follows:

[0116]

[0117] In polar coordinate system:

[0118] Speed ​​prediction value:

[0119] Heading prediction value:

[0120] Distance change rate:

[0121] Distance change rate prediction value:

[0122] Distance acceleration prediction value:

[0123] in,

[0124] Step (c): Based on probability theory, the trace data and the data after coordinate transformation are sent to the multi-factor variance estimator. The multi-factor variance estimator calculates the variance estimates of the multi-factors based on the error propagation method, which are:

[0125] Speed ​​variance:

[0126] Heading variance:

[0127] Distance acceleration variance:

[0128] in:

[0129] T is the radar scanning period;

[0130]

[0131] Step (d): The gate calculator of this embodiment uses a multiple adaptive gate method, which are:

[0132] (1) Azimuth range adaptive sector gate: based on the track position prediction value As the center, select the points (ρ k+1 ,θ k+1 ), determined as a preliminary candidate point trace. k+1 ,θ k+1 )satisfy: Gate distance ρ len and orientation θ len The scope is defined as follows:

[0133]

[0134] in, Among them I A (x) is the characteristic function, defined as follows:

[0135]

[0136] (2) Multi-factor adaptive wave gate: including speed wave gate, heading wave gate, and distance acceleration wave gate. The trace after the azimuth distance adaptive sector wave gate (ρ k+1 ,θ k+1 ), if the traces are simultaneously in the adaptive multi-factor gate, they are determined as the final candidate traces. The specific definition is as follows:

[0137] Speed ​​gate: dot velocity V k+1 satisfy:

[0138] Heading gate: Point track heading C k+1 satisfy:

[0139] Distance acceleration gate: point distance acceleration satisfy:

[0140] in:

[0141]

[0142] Figure 4 They are the upper and lower bounds of the speed, heading, and range acceleration errors of the track target and the actual change curves, namely the gate change curves; Figure 5 This graph shows the mean square error (MSE) of the target's velocity, heading, and range acceleration. As the target's distance and motion state change, the velocity and heading gates adapt to the target, and the target's MSE follows the same pattern as the gates. Using multi-factor gates, the vast majority of false track points can be filtered out.

[0143] Step (e): The outputs of the multi-factor variance estimator and the track predictor are sent to the multi-factor confidence interval calculator, and the 3 times standard deviation (3σ) criterion is selected to determine the confidence interval. According to statistical knowledge, the corresponding confidence level will be greater than 99%. At this time, the confidence intervals of speed, heading, and range acceleration are defined as:

[0144] Speed ​​factor V k+1 The confidence interval for is:

[0145] Heading factor C k+1 The confidence interval for is:

[0146] Distance acceleration factor The confidence interval for is:

[0147] Step (f): The multi-factor membership calculator receives the calculation results of the multi-factor confidence interval calculator and the multi-factor variance estimator to obtain the multi-factor membership. In this embodiment, the following membership calculation formula is used:

[0148]

[0149] Among them, s is the factor to be calculated, p(s) is the membership degree of s, and U is the upper bound of the factor error.

[0150] For the above membership calculation formula, the factors to be calculated for the calculation results of the multi-factor confidence interval calculator include:

[0151] Velocity V of the dot k+1 The speed prediction value of the track The absolute value of the difference:

[0152]

[0153] Course of point track C k+1 The heading prediction value of the track The absolute value of the difference:

[0154]

[0155] Distance acceleration of the point trace Acceleration prediction value of distance from track The absolute value of the difference:

[0156]

[0157] The corresponding upper bounds U of the error are and Error propagation and calculation are performed according to the above rules, such as Figure 4 The figures show the speed, heading and distance acceleration error mean square errors of the target track. In this embodiment, when the target moves complexly, the error mean square errors of multiple factors are changing in real time and are related to the azimuth and distance-azimuth covariance.

[0158] Substituted into the membership calculation formula respectively, the membership of the speed factor, heading factor and distance acceleration factor can be obtained as p(V k+1 )、p(C k+1 )and

[0159] Step (g): Calculate the point trace association score using DS evidence theory and filter out false points, specifically including:

[0160] According to the DS evidence theory, the membership calculation results of multiple factors are integrated to calculate the point trace association score, and the point with the highest association score is selected as the association point. The DS evidence theory evaluation method specifically includes:

[0161] Suppose there is a problem that needs to be decided. To solve this problem, the complete set of all possible outcomes is represented by θ, where all elements in θ are mutually exclusive and the number of elements is finite and enumerable. The answer to the problem can only be an element in θ. This set of incompatible times is called the recognition frame θ, which can be expressed as:

[0162] θ={θ1,θ2,…,θ j ,…,θ N}

[0163] Under the recognition framework θ, there are n sets of evidence E1, E2, …, E n ,m1,m2,…,m n is the corresponding basic trust allocation function, and the focal elements are A1, A2, ..., A n , then the DS synthesis rule is:

[0164]

[0165] In this embodiment, the point trace association score is calculated by fusing the membership calculation results of the three factors of speed, heading and range acceleration according to the DS evidence theory.

[0166] According to the three membership calculation results p(V k+1 )、p(C k+1 )and First, set the membership processing method:

[0167]

[0168] In the embodiment of the present invention, n=3. According to the above formula, the probability of processing the membership result and judging that the point trace is a related point is m. V (p), m C (p), m a (p), whose uncertain probabilities are The specific formula for the probability of fusion judgment point trace being a related point is:

[0169]

[0170] The correlation scores of all candidate traces are calculated according to the above formula, and the trace with the largest correlation score is selected as the associated trace.

[0171] In summary, the association method of adaptive decision-making based on the error propagation method in a complex environment in the above-mentioned embodiment can accurately and adaptively calculate the multi-factor wave gate, confidence interval and membership degree corresponding to the point trace through the error propagation theory. When the target motion state is uncertain, the association score calculation is completed according to the DS evidence theory. The parameters are very few and easy to set. The error propagation method used has a small amount of calculation and is easy to implement, which is of great significance in engineering practice.

[0172] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A correlation method for adaptive decision-making based on error propagation in complex environments, characterized by: The following steps are involved: S1: Convert the polar coordinate data of the radar trace to the rectangular coordinate system, and obtain the variance of the trace in the rectangular coordinate system based on the variance of the polar coordinate system of the distance and azimuth; S2: Use the IMM-UKF filtering algorithm to predict the multi-factor prediction information of the track time; S3: Calculate the variance estimate of the multi-factor based on the trace data and the prediction information of the multi-factor; S4: First, establish an azimuth-range adaptive sector wave gate, then use the 4σ criterion based on the variance estimate of multiple factors to establish a multi-factor adaptive multiple gate to effectively filter out false traces. The 4σ criterion is four times the standard deviation criterion. S5: Based on the prediction information of the multiple factors in step S2 and the variance estimate of the multiple factors in step S3, the confidence interval of the multiple factors is established using the 3σ criterion, which is the 3 times standard deviation criterion; S6: Based on the trace data in step S1, the prediction information of the multiple factors in step S2 and the confidence interval of the multiple factors in step S5, the membership degree of the multiple factors is calculated; S7: Based on the membership of the multiple factors in step S6, the correlation scores of the adaptive multiple gate traces are calculated according to the DS evidence theory, and the trace with the highest correlation score is selected as the correlation trace.

2. The method for adaptive decision-making based on error propagation in complex environments according to claim 1, characterized in that: In step S1, the polar coordinate data of the radar trace is converted to a rectangular coordinate system, and the conversion result is: x k =ρ k cosθ k y k =ρ k sinth k Among them, (ρ k ,θ k ) is the polar coordinate data of the point at time k, (x k ,y k ) is the rectangular coordinate system data of the point trace at time k; According to the variance of the polar coordinate system where the distance and azimuth are located, the variance of the point trace in the rectangular coordinate system is obtained. The definition is as follows: in, are all parameters required for unbiased correction. are the distance and azimuth (ρ k ,θ k ) is the measurement error variance.

3. The method for adaptive decision-making based on error propagation in complex environments according to claim 2, characterized in that: In step S2, take the track time t k is the kth moment, t k+1 If the time is k+1, the IMM-UKF filtering algorithm is used to predict the track information to the point track time t k+1 time, get t k+1 The track prediction value at the moment, that is, the prediction information of multiple factors, is expressed as follows: Speed ​​prediction value: Heading prediction value: Distance change rate: Distance change rate prediction value: Distance acceleration prediction value: in, is the track position prediction value in the rectangular coordinate system, is the track position prediction value in the polar coordinate system, 4. The method for adaptive decision-making based on error propagation in complex environments according to claim 3, characterized in that: In step S3, the variance estimates of the multiple factors are as follows: Speed ​​variance: Heading variance: Distance acceleration variance: in: T is the radar scanning period; 5. The method for adaptive decision-making based on error propagation in complex environments according to claim 4, characterized in that: In step S4, the process of establishing the azimuth distance adaptive sector gate is as follows: As the center, select the points within the distance and direction confidence range (ρ k+1 ,θ k+1 ), determined as a preliminary candidate point trace, where the distance and direction confidence range form a fan-shaped gate.

6. The method for adaptive decision-making based on error propagation in complex environments according to claim 5, characterized in that: In step S4, the adaptive multi-gates of the multiple factors are as follows: Speed ​​gate: dot velocity V k+1 satisfy: Heading gate: Point track heading C k+1 satisfy: Distance acceleration gate: point distance acceleration satisfy: in: When a point is within the range of the azimuth distance adaptive sector gate and also within the range of the adaptive multi-factor gate, it is determined as the final candidate point, thus achieving effective filtering of false points.

7. The method for adaptive decision-making based on error propagation in complex environments according to claim 6, characterized in that: In step S5, the confidence intervals of the multiple factors are the confidence intervals of speed, heading and distance acceleration, which are defined as: Speed ​​factor V k+1 The confidence interval for is: Heading factor C k+1 The confidence interval for is: Distance acceleration factor The confidence interval for is:

8. The method for adaptive decision-making based on error propagation in complex environments according to claim 7, characterized in that: In step S6, the membership degree calculation formula is as follows: Where s is the factor to be calculated, p(s) is the membership degree of s, and U is the upper bound of the factor error; The factors to be calculated include: Velocity V of the dot k+1 The speed prediction value of the track The absolute value of the difference: Course of point track C k+1 The heading prediction value of the track The absolute value of the difference: Distance acceleration of the point trace Acceleration prediction value of distance from track The absolute value of the difference: The upper bounds U of the errors corresponding to the factors to be calculated are and Substitute the above factors to be calculated and the corresponding upper bound of the error into the membership calculation formula to calculate the membership p(V k+1 ), p(C k+1 )and 9. The method for adaptive decision-making based on error propagation in complex environments according to claim 8, characterized in that: In step S7, the membership calculation results of the three factors of speed, heading and distance acceleration are fused according to the DS evidence theory to calculate the point trace association score. The data is fused after processing according to the membership calculation results of the three factors. k+1 ), p(C k+1 )and First set the degree of membership as follows: The probability of judging a point as a related point is m V (p), m C (p), m a (p), whose uncertain probabilities are The specific formula for the probability of fusion judgment point trace being a related point is: The correlation scores of all candidate traces are calculated according to the above formula, and the trace with the largest correlation score is selected as the associated trace.

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