An improved adaptive tracking door target tracking method

Abstract

The application belongs to the field of data association in radar target tracking, and particularly relates to an improved adaptive tracking gate target tracking method; the method can dynamically adjust the transfer probability parameter according to the target motion characteristics, and combines the measurement prediction values and the covariance matrix of each model to form a unified measurement prediction value and covariance matrix. In addition, based on the unified measurement prediction value and the measurement prediction covariance matrix, an adaptive position association wave gate is constructed to effectively screen the measurement values and reduce the calculation amount. In order to cope with the exponential growth problem of the joint matrix in multi-target tracking, the JPDA method is improved, a threshold is set to remove small probability events, so as to reduce the calculation complexity. Compared with the current IMM-PDA method, the method can realize effective tracking of multiple targets in a noisy environment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of data association in radar target tracking, and particularly relates to an improved adaptive tracking gate target tracking method. BACKGROUND

[0002] Radar target tracking refers to the process of using a radar system to continuously observe and measure targets, thereby obtaining real-time information on the motion state of the targets (such as position, velocity, acceleration, etc.). The transmitting antenna of the radar system sends out radio waves, which are reflected back when encountering targets and are received by the receiving antenna of the radar system. Through processing and analysis of the received reflected waves, the radar system can calculate information such as the distance, speed, azimuth, and elevation angle of the targets, and thus achieve tracking of the targets. In radar target tracking, the accuracy of data association directly affects the performance and reliability of the tracking system. When the radar system receives observation data of multiple targets, it needs to determine which target each piece of data belongs to, in order to ensure the continuity and accuracy of tracking.

[0003] Interacting Multiple Model (IMM) is a multi-model method for state estimation of systems with time-varying characteristics, first introduced by Shalom in 1989. The IMM method has excellent performance in both estimation accuracy and computational complexity, and occupies a dominant position in the field of maneuvering target tracking. Nevertheless, the traditional IMM method still needs to be optimized. There are three main ways of optimization: first, changing the size of the model set, increasing the number of models to include more models to cope with the multiple motion states of the target, and achieving robust performance. Second, optimizing the types of model set, striving to solve the complex maneuvering of the target with fewer models. For example, models such as CV, CT, and CS are included in the model set. Third, improving the filter used by the sub-model. The traditional IMM method uses Kalman Filter (KF) as the filter for each model, which can be modified to use non-linear filter methods such as Unscented Particle Filter as the filter for the IMM method. The IMM model set should include as many models as possible that are close to the true motion, so that the tracking is more accurate. In addition, the Markov transition probability matrix determines the interaction and switching between models, so it strongly affects the tracking performance of the IMM method. However, the fixed prior transition probability in the standard IMM has irrationality, and the transition probability parameters cannot be adaptively and dynamically adjusted according to the motion characteristics of the target, affecting the accuracy of target tracking.

[0004] The track gate plays an important role in target tracking, and is used iteratively in track initiation and data association filtering process, which is designed to ensure tracking accuracy while effectively reducing the number of measurements. The track gate is a sub-space in the tracking space, the center of which is located at the predicted position of the tracked target, and the size of which is determined by the probability of receiving correct measurements. The area is used to determine whether there is a target that needs to be tracked, and the size of the gate determines the number of verified measurements. Only those measurements located in the gate, called candidate measurements, will be associated with the existing trajectory. Candidate measurements may be real measurements or clutter. The probability of detecting real measurements through the correlation gate is called detection probability, and vice versa. Commonly used tracking gates include rectangular tracking gates, ellipsoidal tracking gates, etc. Using these tracking gates can effectively remove invalid observation data and reduce computational complexity. However, these tracking gates cannot be self-adapted, resulting in high computational complexity and reducing the accuracy of target tracking to some extent. SUMMARY

[0005] Based on the problems existing in the prior art, the application describes an improved adaptive tracking gate target tracking method, which improves the IMM model, the tracking gate and the data association method to improve the tracking accuracy and reduce the computational complexity of the system. Specifically, the following steps are included: first, the measurement prediction value and the measurement prediction covariance matrix of each model in the interactive multiple model are weighted by the probability of each model to form a unified measurement prediction value and a measurement prediction covariance matrix. Based on the calculated unified measurement prediction value and measurement prediction covariance matrix, an adaptive position tracking gate is established to screen the measurement value. An improved JPDA method is used to calculate the association probability of each valid measurement to obtain centralized measurements for correcting the prediction value of each sub-model. According to the change of the probability of each model, the transition probability matrix is adjusted to realize more effective tracking of the target.

[0006] The improved adaptive tracking gate target tracking method of the application comprises:

[0007] 101, using a sensor to receive observation data from a target in real time; the observation data of the target is the actual observation value of the target at the current time;

[0008] 102, calculate the initial value of each sub-model in the interactive multiple model based on the preset transition probability parameter and predict the target respectively, and obtain the unified prediction value of the target at the current time by probability weighting; the unified prediction value includes a unified measurement prediction value and a unified measurement prediction covariance matrix;

[0009] 103, calculate the adaptive tracking gate according to the unified measurement prediction value and the unified measurement prediction covariance matrix of the target at the current time;

[0010] 104. Filtering the actual observation value of the target at the current time according to the adaptive track gate to obtain the effective observation value of the target at the current time;

[0011] 105. Using the improved joint probability data association method to probabilistically associate the effective observation value of the target at the current time to obtain the centralized unified observation value of the target at the current time; the centralized unified observation value of the target at the current time is used to update the motion state estimation value of the target at the current time;

[0012] 106. Updating the transition probability parameters of each sub-model in the interactive multi-model according to the motion state estimation value of the target at the current time;

[0013] 107. Repeating steps 101-106 until the target tracking is ended.

[0014] Advantages of the present application:

[0015] The present application can dynamically adjust the transition probability parameters according to the target motion characteristics, and combine the measurement prediction values and covariance matrices of each model to form unified measurement prediction values and covariance matrices. The present application constructs an adaptive position association wave gate based on the unified measurement prediction values and error covariance matrices, effectively filters the measurement values to reduce the amount of calculation. The present application sets a threshold to remove small probability events by improving the joint probability data association method, thereby reducing the computational complexity, solving the problem of exponential growth of the joint matrix in multi-target tracking, and enabling effective tracking of multiple targets in a noisy environment. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flow chart of the adaptive target tracking method of the embodiment of the present application;

[0017] Figure 2 is a schematic diagram of the true motion trajectory of the target of the embodiment of the present application;

[0018] Figure 3 is a schematic diagram of the predicted motion trajectory of the target in a clutter environment of the embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0020] The application describes an improved adaptive tracking gate target tracking method, which is improved from an IMM model, a tracking gate and a data association method to improve tracking accuracy and reduce the calculation amount of the system.

[0021] The following is a detailed introduction to the method:

[0022] 101, using a sensor to receive observation data from a target in real time; the observation data of the target is the actual observation value of the target at the current time;

[0023] In the embodiment of the application, the sensor is a device that can convert various information such as physical quantities and chemical quantities into electrical signals or digital signals that can be processed by a computer or other equipment. For example, in an intelligent transportation system, radar sensors, camera sensors and the like can be used as sensors. The observation data of the target is the most real and direct reflection of the target at the current time, i.e. the actual observation value of the target at the current time. These actual observation values carry rich information, which presents various states and characteristics of the target at a specific instant in the form of data. For vehicles in an intelligent transportation system, observation data can include vehicle speed, position, direction of travel and the like.

[0024] 102, based on a preset transition probability parameter, calculating an interaction initial value of each sub-model in the interactive multiple model and predicting the target respectively, and obtaining a unified prediction value of the target at the current time through probability weighting; the unified prediction value includes a unified measurement prediction value and a unified measurement prediction covariance matrix;

[0025] In the embodiment of the application, it is assumed that an interactive multiple model IMM with N sub-models follows a Markov process guided by a Markov chain of limited length when jumping, the state transition probability Π is as shown in formula (1), each transition probability in the state transition matrix is as shown in formula (2), and the sum of the model transition probabilities of each state is 1 as shown in formula (3).

[0026] Π=[p ij ] N×N (1)

[0027] p ij= P {r(k) = j | r(k - 1) = i} (2)

[0028]

[0029] Let the model probability of model i at time k - 1 be The model transition probability p ij Through the interaction calculation, the model mixing probability of model i to model j can be obtained The value is the size ratio of the model transition probability of model i to model j in all transition probabilities of the model to model j, as shown in equation (4):

[0030]

[0031] Wherein As shown in equation (5):

[0032]

[0033] N represents the number of sub-models, and the state estimation of the target at time k - 1 is The state posterior covariance matrix is Through the model mixing probability weighting, the model j mixed input initial state at time k - 1 can be obtained And the initial covariance matrix As shown in equations (6) and (7):

[0034]

[0035] Wherein, is the optimal state estimation value of sub-model i at time k - 1. The initialization is preset, and the subsequent used value is the recursively obtained value.

[0036]

[0037] Wherein, Indicates the initial covariance matrix of model j obtained by model mixing probability weighting, is the posterior covariance matrix of sub-model i at time k - 1. The initialization is preset, and the subsequent used value is the recursively obtained value.

[0038] After the initial state value and the initial covariance matrix of each model are obtained by calculation, the filtering calculation can obtain the prior state prediction value, the prior error covariance matrix, the measurement prediction value, the measurement prediction covariance matrix and the like of each model.

[0039] Therefore, the unified measurement prediction value forming the center of the adaptive tracking gate can be calculated by equation (8), that is:

[0040]

[0041] wherein, represents the unified measurement prediction value from the k-1 time to the k time, is the measurement prediction value of the model i from the k-1 time to the k time, is the prediction probability of the model i at the k time.

[0042] Further, the covariance matrix of the unified measurement prediction value can be obtained through equation (9):

[0043]

[0044] In the above equation, S k is the unified measurement prediction covariance matrix at the k time, is the measurement prediction covariance matrix of the model i.

[0045] 103. The adaptive tracking gate is calculated according to the unified measurement prediction value and the unified measurement prediction covariance matrix of the target at the current time;

[0046] In the embodiment of the present application, the unified measurement prediction value and the unified measurement prediction covariance matrix of the target at the current time are taken as input parameters. In this process, the dynamic characteristics of the target, the environmental noise, the measurement error and other factors are comprehensively considered, and through a series of matrix operations, logical judgments and optimization adjustments, the range of the adaptive tracking gate can be finally determined according to equation (8) and equation (9).

[0047] 104. The actual observation value of the target at the current time is screened according to the adaptive tracking gate, and the effective observation value of the target at the current time is obtained;

[0048] In the embodiment of the present application, the step 104 can include the following steps:

[0049] The center of the adaptive tracking gate at the current time is obtained according to the sum of the product of the measurement prediction value of each sub-model in the interacting multiple model at the current time and the prediction probability of the corresponding sub-model;

[0050] Based on the center of the adaptive tracking gate at the current time and the unified measurement prediction covariance matrix, the adaptive tracking gate is established to screen the received actual observation value;

[0051] If there is an actual observation value falling into the adaptive tracking gate, the position and size of the adaptive tracking gate are not changed;

[0052] If there is no actual observation value falling into the adaptive tracking gate, the adaptive tracking gate is enlarged by increasing the unified measurement prediction covariance matrix to increase the probability of falling into the actual observation value.

[0053] In the embodiment of the application, whether the actual observation value is a valid observation value can be determined by judging whether the actual observation value is within the threshold of the adaptive tracking gate.

[0054] Specifically, based on the unified position tracking gate center and the measurement prediction covariance matrix, the adaptive tracking gate is established to screen the received measurement values. If there is an actual observation value falling within the adaptive tracking gate, the position and size of the adaptive tracking gate are not changed. If there is no actual observation value falling within the adaptive tracking gate, the adaptive tracking gate is appropriately enlarged by increasing the unified measurement prediction covariance matrix, as shown in equation (10), to increase the probability of falling within the observation value.

[0055]

[0056] In equation (10), S represents the unified measurement prediction covariance matrix after amplification at time k, S k represents the unified measurement prediction covariance matrix at time k; is the i-th expansion step, which is set as the covariance matrix of the sub-model corresponding to the maximum maneuvering level at time k; p is the number of tracking gate expansion, which is generally in the range of 3-5 in actual situations; H is an observation matrix, and the superscript T is the matrix transpose. Assuming that the maneuvering level of model j" at time k is the maximum, equation (11) is satisfied

[0057]

[0058] wherein j" represents the sub-model corresponding to the maximum maneuvering level, represents the unified measurement prediction value from time k-1 to time k, represents the measurement prediction value of the sub-model j from time k-1 to time k, and the superscript T represents the transpose. At this time which can reflect the measurement prediction covariance matrix of the sub-model corresponding to the maximum maneuvering level at time k.

[0059] After the operation, if there is an actual observation value falling within the adaptive tracking gate, the center position of the tracking gate is adjusted according to the measurement value within the adaptive tracking gate.

[0060]

[0061] wherein, represents the j'th observation point information falling within the relevant wave gate obtained by the kth scanning, that is, the j'th valid observation value. m k represents the total number of measurement points falling within the relevant wave gate obtained by the kth scanning. and respectively represent the position coordinates of the j'th valid measurement in the k'th scan in the spatial rectangular coordinate system. The center position of the adjusted adaptive tracking gate can be obtained by equation (13).

[0062]

[0063] wherein, represents the center position of the adaptive tracking gate determined in the k'th scan after adjustment.

[0064] In the preferred embodiment of the present application, the size of the adaptive tracking gate is changed back to the size before amplification within a preset period, so as to remove more false measurements and reduce the calculation amount of data association.

[0065] 105. The valid observation value of the target at the current time is probabilistically associated using the improved joint probability data association method to obtain centralized unified observation value of the target at the current time; the centralized unified observation value of the target at the current time is used to update the motion state estimation value of the target at the current time;

[0066] In the embodiment of the present application, the step 105 can include:

[0067] According to the probability of the valid observation value falling into the adaptive tracking gate, the measurement innovation, the covariance matrix and the clutter density, the approximate posterior probability of the valid observation value and the target association is calculated;

[0068] If the valid observation value at the current time is only in one wave gate, the measurement association probability does not need to be adjusted;

[0069] If the valid observation value at the current time is in the intersecting wave gate, the measurement association probability needs to be adjusted;

[0070] The centralized unified observation value of the target at the current time is calculated by weighted summation according to the calculated association probability of each valid observation value.

[0071] Specifically, the valid observation value in the latest adaptive tracking gate is calculated by using the improved JPDA method to calculate the association probability of the valid observation value. The improved JPDA data association method is to first approximately calculate the posterior probability, and then remove the event with lower posterior probability to reduce the calculation amount of splitting. If there is only one target or the tracking wave gates are not intersected, the posterior probability calculation formula is:

[0072]

[0073] wherein, represents the association probability of the valid observation value j' falling into one wave gate of the tracking wave gate and the target t at the current time, wherein equation (14) is:

[0074]

[0075] where m k is the total number of valid observations within the target t's gate, P D is the detection probability of the target t, P G is the probability that a valid observation falls into the gate, is the innovation of the valid observation, which is obtained by subtracting the measurement prediction of each sub-model from the actual observation, S k is the unified measurement prediction covariance matrix at time k, and λ is the clutter density.

[0076] If the valid observation j is only in one gate, the probability does not need to be changed, and if the valid observation j is in the intersection gate, the probability needs to be reduced, and the specific formula is formula (17):

[0077]

[0078] wherein represents the association probability of the valid observation j' falling into the intersection area of the tracking gate and the target t after the current time correction, and α is the attenuation coefficient.

[0079] After calculating the source probability of each valid observation, a threshold is set, the event with a probability less than the threshold is considered as an impossible event, and the corresponding position of the confirmation matrix is set to zero, as shown in formula (18), and then the final association probability is calculated according to the steps of the JPDA method. ε is the threshold, and by setting the threshold, small probability events can be removed, thereby reducing the calculation complexity.

[0080]

[0081] The unified observation value is obtained by weighted sum of the final association probability and the corresponding observation value calculated, as shown in formula (19):

[0082]

[0083] wherein m k is the total number of valid observations, is the j'th valid observation, represents the final association probability of the valid observation j' and the target t at the current time k calculated by the decomposed and corrected confirmation matrix. The calculated unified observation value is used to update the subsequent posterior state estimation, posterior error covariance matrix and probability value of each model.

[0084] The model likelihood function Λ j (k) and the model transition probability are updated to update the probability of the model j at the current time k:

[0085]

[0086] wherein The likelihood function is given by equation (21) :

[0087]

[0088] wherein is the filter innovation of model j at time k, is the measurement prediction covariance matrix of model j at time k.

[0089] The total state estimate and error covariance matrix of the target at time k can be obtained as shown in equations (22) and (23) :

[0090]

[0091] wherein, is the optimal state estimate at time k, is the optimal state estimate of sub-model i at time k, is the model probability of model i at time k, P kk is the posterior covariance matrix at time k, is the posterior covariance matrix of sub-model i at time k.

[0092] 106. updating the transition probability parameters of each sub-model in the interacting multiple model according to the motion state estimate of the target at the current time;

[0093] In the embodiments of the present application, the step 106 can specifically include:

[0094] updating the transition probability of each sub-model in the interacting multiple model at the current time according to the model likelihood function and the transition probability parameters;

[0095] constructing a correction function according to the model likelihood function to adaptively adjust the state transition probability of each sub-model in the interacting multiple model;

[0096] after adjusting the transition probability matrix using the correction function, comparing the sizes of all diagonal elements and the first parameter and the sizes of all non-diagonal elements and the second parameter;

[0097] if the transition probability matrix is greater than the first parameter, using a first transition calculation function to calculate the transition probability of each sub-model in the interacting multiple model;

[0098] if the transition probability matrix is less than the second parameter, using a second transition calculation function to calculate the transition probability of each sub-model in the interacting multiple model.

[0099] Specifically, based on the likelihood function of the model, the transition probability matrix of IMM is adaptively adjusted according to equations (24), (25), and (26) to make the model transition probability values more reasonable.

[0100]

[0101] In order to address the issue that as the iterative calculation progresses, the transition probability from a non-matching model to a matching model becomes increasingly large, resulting in the transition probability of the non-matching model becoming smaller and smaller. If the transition probability of the model is very small when the non-matching model becomes a matching model, it will cause a lag in model jumps and a decrease in the efficiency of model state switching. Therefore, element adjustment is required to ensure that the values are not too low. A threshold, that is, the first parameter a and the second parameter b, can be set for the diagonal elements and non-diagonal elements of the transition matrix respectively. After adjusting the transition probability matrix using the correction function, compare the sizes of all diagonal elements with a and the sizes of non-diagonal elements with b. When p ii (k)>a, adjust the transition probability size according to equation (27):

[0102]

[0103] where p ii (k) represents the probability that the sub-model i transfers to the sub-model i at time k, p ij (k) represents the probability that the sub-model i transfers to the sub-model j at time k, p in (k) represents the probability that the sub-model i transfers to the sub-model n at time k, a represents the first parameter; b represents the second parameter. When p ij (k)<b, the method of correcting the transition probability size is similar to the above formula. By dynamically adjusting the transition probability values between models and keeping them within a reasonable range, the switching speed of the model and the robustness of the method can be improved, achieving better tracking performance. After the transition probability adjustment is completed, continue with the next recursive operation.

[0104] 107. Repeat steps 101 - 106 until the target tracking ends.

[0105] Through the above steps, continuous tracking of the target can be achieved.

[0106] In some embodiments, the following test conditions are given in this embodiment: Assume that the probability P D of detecting a moving target is 1, the probability P G that the correct measurement falls within the tracking gate is 0.99, the threshold of the adaptive tracking gate is g_sigma with a value of 5, the number of simulated tracking targets is 4, and the interference in the target motion space is Gaussian noise with a density of 2*10 -9 per unit volume. Assume that all 4 targets perform uniformly accelerated linear motion, and under the above conditions, simulate to verify the effectiveness of this method.

[0107] Figure 2 The real motion trajectories of multiple targets generated by simulation are displayed; Figure 3 The results of using this method to track a moving target in the presence of clutter interference are shown. It can be seen that the estimated motion trajectory can well reflect the simulated real motion trajectory, indicating that this method has certain robustness and accuracy in interference environments.

[0108] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: ROM, RAM, disk or CD, etc.

[0109] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An improved adaptive tracking gate target tracking method, characterized by, The method comprises:

101. Real-time receiving observation data from the target using a sensor; the observation data of the target is an actual observation value of the target at the current time; 102. Calculating an interaction initial value of each sub-model in the interacting multiple model based on a preset transition probability parameter, and respectively predicting the target, and obtaining a unified prediction value of the target at the current time through probability weighting; the unified prediction value comprises a unified measurement prediction value and a unified measurement prediction covariance matrix; 103. Calculating an adaptive tracking gate according to the unified measurement prediction value and the unified measurement prediction covariance matrix of the target at the current time; 104. Screening the actual observation value of the target at the current time according to the adaptive tracking gate, and obtaining an effective observation value of the target at the current time; 105. Probabilistically associating the effective observation value of the target at the current time using an improved joint probabilistic data association method, and obtaining a centralized unified observation value of the target at the current time; the centralized unified observation value of the target at the current time is used to update a motion state estimation value of the target at the current time; The improved joint probabilistic data association method is used to probabilistically associate the effective observation value of the target at the current time, and obtain the centralized unified observation value of the target at the current time, which comprises: According to the probability of the effective observation value falling into the adaptive tracking gate, the measurement innovation, the covariance matrix and the clutter density, the approximate posterior probability of the effective observation value and the target association is calculated; If the effective observation value at the current time is only in one wave gate, the measurement association probability does not need to be adjusted; If the effective observation value at the current time is in an intersection wave gate, the measurement association probability needs to be adjusted; According to the weighted summation of the calculated association probability of each effective observation value, the centralized unified observation value of the target at the current time is calculated; If the effective observation value at the current time is in an intersection wave gate, the calculation formula for adjusting the measurement association probability comprises: wherein, represents the association probability of the effective observation value j' falling within the intersection region of the tracking gates and the target t at the current time after correction, and a is a decay coefficient, and n represents the total number of targets, represents the association probability of the effective observation value j' falling within only one tracking gate and the target t at the current time.

106. Updating the transition probability parameter of each sub-model in the interacting multiple model according to the motion state estimation value of the target at the current time; 107. Repeating steps 101-106 until the target tracking is completed.

2. The improved adaptive tracking gate target tracking method according to claim 1, wherein, The adaptive tracking gate is used to screen the actual observation value of the target at the current time, and obtain the effective observation value of the target at the current time, which comprises: According to the sum of the product of the measurement prediction value of each sub-model in the interacting multiple model at the current time and the prediction probability of the corresponding sub-model, the unified measurement prediction value at the current time, i.e. the center of the adaptive tracking gate, is obtained; Based on the center of the adaptive tracking gate at the current time and the unified measurement prediction covariance matrix, the adaptive tracking gate is established to screen the received actual observation value; If there is an actual observation value falling into the adaptive tracking gate, the position and size of the adaptive tracking gate are not changed; If there is no actual observation value falling into the adaptive tracking gate, the adaptive tracking gate is enlarged by increasing the unified measurement prediction covariance matrix to increase the probability of the actual observation value falling into.

3. The improved adaptive tracking gate target tracking method according to claim 2, wherein, The adaptive tracking gate is enlarged to the size before the enlargement in a preset period after the actual observation value of the target at the current time is filtered according to the adaptive tracking gate.

4. The improved adaptive tracking gate target tracking method according to claim 2, wherein, The formula for enlarging the adaptive tracking gate by increasing the unified measurement prediction covariance matrix is: wherein, denotes the scaled measurement prediction covariance matrix at time k, is the i-th extended step size; S k denotes the scaled measurement prediction covariance matrix at time k, p is the number of adaptive tracking gating; H is the observation matrix, and the superscript T is the matrix transpose.

5. The improved adaptive tracking gate target tracking method according to claim 4, wherein, The i-th extended step is determined by the measurement prediction value of the sub-model corresponding to the maximum maneuvering level and the unified measurement prediction covariance matrix, and the calculation formula of the sub-model corresponding to the maximum maneuvering level is: where j" represents the sub-model corresponding to the maximum maneuver level, represents the unified measurement prediction value from time k-1 to time k, represents the measurement prediction value of the sub-model j from time k-1 to time k, the superscript T represents transposition, and N represents the number of sub-models.

6. The improved self-adapting tracking gate object tracking method of claim 1, wherein, The updating of the transition probability parameters of each sub-model in the interactive multiple model according to the motion state estimation value of the target at the current time comprises: The transition probability of each sub-model in the interactive multiple model at the current time is updated according to the model likelihood function and the transition probability parameters; The state transition probability of each sub-model in the interactive multiple model is adaptively adjusted according to the correction function constructed according to the model likelihood function; After the transition probability matrix is adjusted using the correction function, the sizes of all diagonal elements and the first parameter and the sizes of all non-diagonal elements and the second parameter are compared; If the transition probability matrix is greater than the first parameter, the transition probability of each sub-model in the interactive multiple model is calculated using the first transition calculation function; If the transition probability matrix is less than the second parameter, the transition probability of each sub-model in the interactive multiple model is calculated using the second transition calculation function.

7. The improved adaptive tracking gate target tracking method of claim 6, wherein, The calculation formula of the correction function for adaptively adjusting each sub-model in the interactive multiple model according to the model likelihood function comprises: where p ii (k) represents the probability of submodel i transferring to submodel i at time k, p ij (k) represents the probability of submodel i transferring to submodel j at time k, p in (k) represents the probability of submodel i transferring to submodel n at time k, a represents a first parameter; and b represents a second parameter.

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