A radar target tracking method

Abstract

The present application provides a radar target tracking method, which obtains pulse echo data collected by the radar; based on the pulse echo data after sampling and processing, generates a target prediction state corresponding to the pulse echo data. The target prediction state is the prediction data of the motion state of the target to be tracked at the next moment. Based on a preset formula, the detection threshold in the prediction tracking gate corresponding to the target prediction state is determined. The preset formula is the relationship between the number of distance resolution units in the prediction tracking gate and the detection threshold. The distance resolution unit is the sampling interval under the condition of sampling and processing of the pulse echo data. Through the detection threshold, the pulse echo power corresponding to the tracking target at the next moment is detected to determine the tracking motion state of the tracking target at the next moment. Through the above method, the radar can adjust the detection threshold in real time to avoid the technical problem of target track interruption caused by fluctuations in target echo power.

Description

Technical Field

[0001] The present application relates to the field of radar tracking technology, and in particular to a radar target tracking method. Background Art

[0002] Bird strikes, also known as bird strikes, refer to the collision of birds with flying man-made aircraft, high-speed trains, cars, etc., causing damage. Therefore, radars are generally installed on aircraft to monitor and detect flying bird targets to ensure the safety of air routes.

[0003] When the radar detects flying birds, the radar may lose the tracking target due to the constant changes in the radar's observation angle and the target's attitude, resulting in missing radar measurement points and failure to fully track the target. In the process of changing the radar's observation angle and the target's attitude, the target echo power will fluctuate irregularly, which may cause missing measurement points and interrupt the radar's target tracking track. Summary of the invention

[0004] The embodiment of the present application provides a radar target tracking method for solving the technical problem of target tracking track interruption caused by target echo power fluctuation during radar target tracking.

[0005] On the one hand, the present application provides a radar target tracking method, the method comprising:

[0006] Obtain the pulse echo data collected by the radar. Generate the target prediction state corresponding to the pulse echo data based on the sampled pulse echo data. The target prediction state is the prediction data of the motion state of the target to be tracked at the next moment. Based on the preset formula, determine the detection threshold in the prediction tracking gate corresponding to the target prediction state. The preset formula is the relationship between the number of distance resolution units in the prediction tracking gate and the detection threshold. The distance resolution unit is the sampling interval under the sampling and processing of the pulse echo data. Through the detection threshold, the pulse echo power corresponding to the tracking target at the next moment is detected to determine the tracking motion state of the tracking target at the next moment.

[0007] In one implementation of the present application, it is determined whether a tracked target corresponding to the pulse echo data has a recorded track. If a tracked target corresponding to the pulse echo data has a recorded track, the detection threshold is calculated based on the parameters in the detection probability corresponding to the power fluctuation model of the tracked target and the number of distance resolution units in the predicted tracking gate. The power fluctuation model is a Weibull distribution model obtained by fitting the recorded track data through a goodness of fit test method.

[0008] In one implementation of the present application, the preset formula is as follows:

[0009]

[0010] in, is the shape parameter in the detection probability, is the scale parameter in the detection probability, is the number of distance resolution units, is the detection threshold.

[0011] In one implementation of the present application, it is determined whether there is a corresponding recorded track for the tracked target. If so, the motion state of the tracked target at the previous moment is determined in the recorded track. According to the motion state of the tracked target at the previous moment and the target measurement state at the current moment, the change in maneuvering acceleration at the current moment is determined. According to the change in maneuvering acceleration, the acceleration variance at the current moment is determined. Based on the acceleration variance, the target prediction state is determined. Among them, the target measurement state is obtained based on the pulse echo data after sampling processing.

[0012] In one implementation of the present application, based on a reference window of a preset number of frames, the acceleration change at each adjacent moment in the reference window is determined. The acceleration value is the acceleration value of the target prediction state at the corresponding moment. The corresponding acceleration change at each moment before the previous moment is determined, and a set of acceleration changes is generated. The maneuvering frequency weight is determined based on the acceleration change at the previous moment and the maximum value in the set of acceleration changes. The maneuvering frequency at the current moment is determined based on the maneuvering frequency weight and the maneuvering frequency of the target tracked at the previous moment. Based on the acceleration variance at the current moment and the maneuvering frequency at the current moment, an operating state equation for generating the target prediction state at the next moment is determined to determine the target prediction state through the motion state equation.

[0013] In one implementation of the present application, the target prediction state at the current moment is determined according to the motion state of the target tracked at the previous moment, and a one-step prediction function corresponding to the target prediction state is generated. The target measurement state at the current moment is determined according to the pulse echo data after sampling and processing, and a measurement function corresponding to the target measurement state is generated. The average of the difference between the measurement function and the one-step prediction function is used as the maneuvering acceleration change at the current moment.

[0014] In one implementation of the present application, the error between the tracking motion state of the tracking target and the actual motion state of the tracking target within a preset time is determined as the success tracking rate. When the success tracking rate is less than a preset threshold, a tracking prompt message is generated. The tracking prompt message includes sound and text. According to the feedback information sent by the user based on the tracking prompt message, the parameters of the preset formula are adjusted.

[0015] In one implementation of the present application, if it is determined that the tracking target corresponding to the pulse echo data does not have a recorded track, the pulse echo data is used as the starting track data of the tracking target. According to the pulse echo power of the starting track data, the detection threshold within the predicted tracking gate in the preset detection threshold table is determined.

[0016] Through the above scheme, the detection threshold of the predicted tracking gate can be updated in real time to avoid the echo power fluctuation of the tracked target, which causes the detection threshold to be unable to detect the measurement point of the tracked target; at the same time, it is avoided that the radar's tracking target trajectory lacks measurement points, causing the tracking target trajectory to be interrupted. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic diagram of a flow chart of a radar target tracking method in an embodiment of the present application;

[0019] Figure 2 Another schematic diagram of a flow chart of a radar target tracking method in an embodiment of the present application;

[0020] Figure 3 A schematic diagram of a performance evaluation in a radar target tracking method in an embodiment of the present application;

[0021] Figure 4 Another schematic diagram of a performance evaluation in a radar target tracking method in an embodiment of the present application;

[0022] Figure 5 It is another schematic diagram of performance evaluation in a radar target tracking method in an embodiment of the present application;

[0023] Figure 6 It is another flow chart of a radar target tracking method in an embodiment of the present application;

[0024] Figure 7 It is a schematic diagram of the structure of a radar target tracking device in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0026] There are about 21,000 bird strikes every year in the world, causing economic losses of about US$1.2 billion. Therefore, effective monitoring and detection of flying birds is of great significance to ensuring air route safety and can bring huge economic and ecological benefits to human society.

[0027] However, when radar detects such targets, on the one hand, the fluctuation of target echo power caused by the change of radar observation angle and target posture can easily lead to the loss of measurement points and thus track interruption; on the other hand, the movement trajectory of flying bird targets is diverse and highly maneuverable, making it difficult to pre-set a suitable filter model set, and model mismatch will lead to a decrease in tracking accuracy. Therefore, the inherent echo power fluctuation and maneuverability of such targets increase the difficulty of radar target tracking processing.

[0028] If the target can be tracked in real time and accurately when the target's echo power fluctuates, it can bring great benefits to the safe navigation of aircraft.

[0029] Based on this, an embodiment of the present application provides a radar target tracking method for real-time and accurate tracking of flying bird targets, thereby reducing the difficulty of radar tracking of flying bird targets.

[0030] The following describes in detail various embodiments of the present application in conjunction with the accompanying drawings.

[0031] The present application embodiment provides a radar target tracking method, such as Figure 1 As shown, the method may include steps S101-S104:

[0032] S101, the server obtains pulse echo data collected by the radar.

[0033] In the embodiment of the present application, the radar can continuously emit pulses and collect pulse echo data of the emitted pulses.

[0034] It should be noted that the server, as the executor of the radar target tracking method, is only an example, and the executor is not limited to the server, and no specific limitation is made to this.

[0035] S102: The server generates a target prediction state corresponding to the pulse echo data according to the sampled pulse echo data.

[0036] The target prediction state is the prediction data of the motion state of the tracking target at the next moment.

[0037] The target prediction state can be determined by the current statistical model. For example, the prior art authorization number is CN107728138B, and the invention name is: A method for tracking a maneuvering target based on a current statistical model. Although the prior art can improve the tracking performance of weak maneuvering and non-maneuvering targets while maintaining the good tracking performance of the current statistical model for strong maneuvering targets. However, the parameters in the current statistical model are generally constants. If the maneuvering frequency and acceleration variance of the target change, it is easy to cause the model to be insufficiently accurate, affecting the determination of the target prediction state. Therefore, the present application provides the following scheme to make the current statistical model perform adaptive parameter adjustment to obtain the target prediction state.

[0038] In the embodiment of the present application, step S102 can generate a target prediction state corresponding to the pulse echo data by executing the following method, such as Figure 2 As shown, the specific steps include:

[0039] S201: The server determines whether there is a corresponding recorded track of the tracking target.

[0040] The server can determine whether there is a track associated with the tracked target according to the motion state of the tracked target, such as position, speed, and acceleration, so as to determine the recorded track of the tracked target. The server can store the data of the tracked target detected by the radar within a period of time, such as 24 hours, in a memory to be called when determining the recorded track.

[0041] S202: The server determines that a corresponding recorded track exists for the tracking target, and determines the motion state of the tracking target at a previous moment in the recorded track.

[0042] In the embodiment of the present application, when the tracking target has a recorded trajectory, if the current time is , the server can determine The motion state at a certain moment, the motion state at least includes: the position coordinates of the tracking target, the speed of the tracking target, and the acceleration of the tracking target.

[0043] S203: The server determines the current maneuver acceleration change according to the motion state of the target tracked at the previous moment and the target measurement state at the current moment, and determines the acceleration variance at the current moment according to the maneuver acceleration change.

[0044] The target measurement state is obtained based on the pulse echo data after sampling and processing.

[0045] In the embodiment of the present application, the maneuvering acceleration change at the current moment is determined by executing the following method, which is as follows:

[0046] First, the server determines the target prediction state at the current moment according to the motion state of the target tracked at the previous moment, and generates a one-step prediction function corresponding to the target prediction state.

[0047] The specific formula of the one-step prediction function is:

[0048]

[0049] in, for Predict the current moment The target prediction state is the one-step prediction position coordinate of the target at the current moment. For the previous moment The target position coordinates, For the previous moment The target speed, For time, For the previous moment The target acceleration.

[0050] Then, the server determines the target measurement state at the current moment according to the pulse echo data after sampling and processing, and generates a measurement function corresponding to the target measurement state.

[0051] The measurement function is specifically:

[0052]

[0053] in, is the target measurement state at the current moment, is the acceleration change caused by the target maneuver, is Gaussian noise with a mean of 0.

[0054] The server uses the average of the difference between the measured function and the one-step prediction function as the maneuver acceleration change at the current moment.

[0055] The difference between the measurement function and the one-step prediction function is:

[0056]

[0057] in, It represents the difference between the measurement function and the one-step prediction function, i.e. the new information; is the change in maneuvering acceleration.

[0058] The mean of the difference between the measured function and the one-step prediction function is:

[0059]

[0060] The server can obtain the acceleration variance at the current moment, calculated as follows:

[0061] .

[0062] S204: The server determines the acceleration change at each adjacent moment in the reference window according to the reference window of the preset frame number.

[0063] The acceleration value is the acceleration value of the target predicted state at the corresponding moment.

[0064] The server may determine a reference window containing a preset number of time frames, where the preset number of frames may be calculated as follows:

[0065]

[0066] in, is the number of frames in the reference window, that is, the number of moments included, is the reference window time length, is the tracking sampling period of the radar.

[0067] The acceleration change can be calculated using the following formula:

[0068]

[0069] in, For the moment The acceleration change, For the moment The acceleration value of the target predicted state, time The acceleration value of the target predicted state.

[0070] S205. The server determines the acceleration changes corresponding to each moment before the previous moment, and generates a set of acceleration changes.

[0071] The server can obtain the corresponding acceleration change at each moment included in the reference window. In the embodiment of the present application, the reference window does not include the previous moment, that is, the previous moment As the boundary, take the time before the previous moment For example, if the previous moment is the origin of the coordinate axis, the reference window is located to the left of the origin of the coordinate axis and its length is .

[0072] The acceleration changes are as follows:

[0073]

[0074] in, is the set of acceleration changes.

[0075] S206. The server determines the maneuvering frequency weight according to the acceleration change at the previous moment and the maximum value in the acceleration change set.

[0076] Specifically, the acceleration change at the previous moment can be calculated by the formula in step S204, and the server can determine the maximum value in the acceleration change set. , then the server can determine the maneuvering frequency weight at the current moment by the following formula:

[0077]

[0078] in, Indicates the maneuver frequency weight at the current moment.

[0079] S207: The server determines the maneuvering frequency at the current moment according to the maneuvering frequency weight and the maneuvering frequency of the tracked target at the previous moment.

[0080] After obtaining the maneuvering frequency weight at the current moment, the server can calculate the product of the maneuvering frequency weight and the maneuvering frequency of the tracked target at the previous moment to obtain the maneuvering frequency at the current moment.

[0081] S208. The server determines an operating state equation for generating a target predicted state at the next moment according to the acceleration variance at the current moment and the maneuvering frequency at the current moment, so as to determine the target predicted state through the motion state equation.

[0082] After the server obtains the acceleration variance and the maneuvering frequency at the current moment, it can determine the parameter values ​​in the operating state equation corresponding to the next moment in the current statistical model, and then obtain the target predicted state at the next moment.

[0083] Through the above scheme, the present application can adaptively adjust the acceleration variance and maneuvering frequency in the current statistical model, realize real-time updating of the acceleration variance and maneuvering frequency, better adapt to the maneuvering characteristics of the target, solve the problem that the filtering model is difficult to match the target with strong maneuverability changes, and improve the filtering accuracy of the current statistical model.

[0084] S103: The server determines the detection threshold within the prediction tracking gate corresponding to the target prediction state based on a preset formula.

[0085] The preset formula is used to predict the relationship between the number of distance resolution units in the tracking gate and the detection threshold. The distance resolution unit is the sampling interval in the case of sampling and processing the pulse echo data.

[0086] In the embodiment of the present application, determining the detection threshold within the prediction tracking gate corresponding to the target prediction state in step S103 specifically includes:

[0087] The server determines whether there is a recorded track for the target corresponding to the pulse echo data.

[0088] In an embodiment of the present application, the radar can determine whether the pulse echo data and the tracked target in the recorded track are the same target by tracking the pulse echo data corresponding to the target, so as to determine whether there is a recorded track for the tracked target corresponding to the pulse echo data.

[0089] When the server determines the tracking target corresponding to the pulse echo data and there is a recorded track, the detection threshold is calculated based on the parameters in the detection probability corresponding to the power fluctuation model of the tracking target and the number of distance resolution units in the predicted tracking gate; wherein the power fluctuation model is a Weibull distribution model obtained by fitting the recorded track data through a goodness of fit test method.

[0090] The preset formula is as follows:

[0091]

[0092] in, is the shape parameter in the detection probability, is the scale parameter in the detection probability, is the number of distance resolution units, The detection threshold can be calculated by binary calculation.

[0093] In the embodiment of the present application, the power fluctuation model has performance expressions for detecting its performance: false alarm probability expression and detection probability expression. The specific formulas are as follows:

[0094]

[0095]

[0096] in, is the false alarm probability expression, is the detection probability expression.

[0097] Through the above scheme, the present application can make real-time adjustment of the detection threshold of the prediction tracking gate, and the above preset formula is obtained when the tracking error covariance is minimized, thereby ensuring the existence of an optimal detection point for the prediction tracking gate, solving the technical problem that the target echo power fluctuations caused by changes in the current radar observation angle and target posture may easily lead to the loss of measurement points, and avoiding track interruption.

[0098] In the embodiment of the present application, when the server determines that the tracking target corresponding to the pulse echo data does not have a recorded track, the pulse echo data is used as the starting track data of the tracking target.

[0099] The server determines the detection threshold within the predicted tracking gate in the preset detection threshold table according to the pulse echo power of the initial track data.

[0100] The preset detection threshold table may be a data table of detection thresholds obtained through crawler software; or it may be set by the user according to needs.

[0101] S104: The server detects the pulse echo power corresponding to the tracking target at the next moment by detecting the threshold, so as to determine the tracking motion state of the tracking target at the next moment.

[0102] After the server determines the tracking motion state of the tracking target at the next moment, the accuracy of the radar target tracking method for implementing the tracking motion state detection can be checked by executing the following method. Specifically, the following method can be executed:

[0103] First, the server determines the error between the tracking motion state of the tracking target and the actual motion state of the tracking target within a preset time as the success tracking rate.

[0104] The calculation formula for the successful tracking rate is as follows:

[0105]

[0106] The preset time is to time, and It is the filtering result, which is based on the one-step prediction function result and the filtering position is obtained according to the current statistical model.

[0107] Then, when the success tracking rate is less than a preset threshold, the server generates tracking prompt information, which includes sound and text.

[0108] In the embodiment of the present application, the preset threshold value can be set according to the actual use requirements, and the present application does not make any specific limitation on this. If the tracking prompt information is text, it can be set to: the target cannot be accurately tracked.

[0109] Finally, the server adjusts the parameters of the preset formula according to the feedback information sent by the user based on the tracking prompt information.

[0110] The radar target tracking method of this application has a significant improvement in the success tracking ratio (STR) of the tracked target compared with the current multiple hypothesis tracking (MHT) algorithm. The study is conducted using multiple groups of target sample data with different fluctuation levels and maneuvering levels. The results of the success tracking ratio are as follows: Figure 3 The curve corresponding to the result of this application is the RTM curve.

[0111] Another example is the Cumulative Number of Track Breakages (CNTB): at the evaluation time The total number of times that the real target was not assigned a track before is called The cumulative number of track interruptions at time is recorded as ,and

[0112]

[0113] in, is the total number of true targets, and For the real goal Until the evaluation time The total number of track interruptions. The comparison result of the cumulative track interruption number of the radar target tracking method in this application and the MHT is shown in the figure below. Figure 4 shown.

[0114] Mean Optimal Subpattern Assignment (MOSPA): The OSPA distance is an error distance used to measure the degree of difference between sets, which can better evaluate the multi-target tracking performance. It is defined as:

[0115]

[0116] in, . n represents the number of measurement points, and m represents the number of targets. Representative Set All permutations and combinations of To select the true target state of each target at time k With filter value One-to-one sorting. The OSPA distance can be decomposed into two parts: positioning error and potential error. The associated sensitivity parameter c determines the relative weight of the potential error part and the positioning error part. That is, a larger c value emphasizes the potential error part, and a smaller c value emphasizes the positioning error part; the distance sensitivity parameter p determines the sensitivity to outliers. In the embodiment of the present application, set , .

[0117] The mean MOSPA of the OSPA distances at multiple moments is defined as:

[0118] (34)

[0119] in, is the starting frame of the steady-state track, is the end frame of the steady-state trajectory.

[0120] The comparison results of MOSPA and MHT algorithms in this application are shown in the figure below. Figure 5 shown.

[0121] The present application can improve the accuracy of detecting thresholds to identify and track targets by adjusting the parameters of a preset formula.

[0122] The above scheme can adapt to the tracking target with high maneuverability through the current statistical model of adaptive parameters, so that the tracking target and the tracking filter model can be accurately matched. At the same time, the corresponding predicted tracking gate is generated by using the accurately matched tracking target, and the tracking target within the predicted tracking gate is determined by calculating the detection threshold corresponding to the preset tracking gate. By using the detection threshold updated in real time, the recognition accuracy of the tracking target can be improved when the target echo power fluctuates, and the track interruption problem caused by the fluctuation of the target echo power can be solved.

[0123] In the embodiment of the present application, the flow chart of the radar target tracking method is as follows: Figure 6 As shown, specifically:

[0124] S601, the server obtains radar echo data;

[0125] S602, the server performs constant false alarm detection on radar echo data;

[0126] S603, the server generates a target measurement state;

[0127] S604, determining whether there is a stable track;

[0128] S605, bird target echo power fluctuation model analysis;

[0129] S606, determining a performance expression of a power fluctuation model;

[0130] S607, track information feedback;

[0131] S608, mean square estimation error between the target measurement state and the target prediction state of the track;

[0132] S609, determining a detection threshold;

[0133] S6010, reconstruction of measurement set within prediction tracking gate;

[0134] S6011, generate a prediction tracking gate measurement set and calculate the probability of each measurement point;

[0135] S6012, generating a starting track;

[0136] S6013, determining whether the track is a start track or a stop track;

[0137] S6014, generating a termination track;

[0138] S6014, adaptive parameter filtering;

[0139] S6015, track status update.

[0140] Figure 7 This is a schematic diagram of the structure of a radar target tracking device in an embodiment of the present application, the device includes:

[0141] At least one processor; and a memory in communication with the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0142] Obtain the pulse echo data collected by the radar. Generate the target prediction state corresponding to the pulse echo data based on the sampled pulse echo data. The target prediction state is the prediction data of the motion state of the target to be tracked at the next moment. Based on the preset formula, determine the detection threshold in the prediction tracking gate corresponding to the target prediction state. The preset formula is the relationship between the number of distance resolution units in the prediction tracking gate and the detection threshold. The distance resolution unit is the sampling interval under the sampling and processing of the pulse echo data. Through the detection threshold, the pulse echo power corresponding to the tracking target at the next moment is detected to determine the tracking motion state of the tracking target at the next moment.

[0143] The embodiment of the present application further provides a non-volatile computer storage medium for radar target tracking, storing computer executable instructions, wherein the computer executable instructions are configured as follows:

[0144] Obtain the pulse echo data collected by the radar. Generate the target prediction state corresponding to the pulse echo data based on the sampled pulse echo data. The target prediction state is the prediction data of the motion state of the target to be tracked at the next moment. Based on the preset formula, determine the detection threshold in the prediction tracking gate corresponding to the target prediction state. The preset formula is the relationship between the number of distance resolution units in the prediction tracking gate and the detection threshold. The distance resolution unit is the sampling interval under the sampling and processing of the pulse echo data. Through the detection threshold, the pulse echo power corresponding to the tracking target at the next moment is detected to determine the tracking motion state of the tracking target at the next moment.

[0145] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0146] The devices, media and methods provided in the embodiments of the present application correspond one to one, and therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0147] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0148] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A radar target tracking method, characterized in that: The method comprises: Acquire pulse echo data collected by radar; Generate a target prediction state corresponding to the pulse echo data according to the pulse echo data after sampling and processing; wherein the target prediction state is the prediction data of the motion state of the tracking target at the next moment; Based on a preset formula, determining a detection threshold in a prediction tracking gate corresponding to the target prediction state; wherein the preset formula is a relationship between the number of distance resolution units in the prediction tracking gate and the detection threshold; the distance resolution unit is a sampling interval in the case of sampling and processing the pulse echo data; By means of the detection threshold, the pulse echo power corresponding to the tracking target at the next moment is detected to determine the tracking motion state of the tracking target at the next moment; Wherein, based on a preset formula, determining the detection threshold within the prediction tracking gate corresponding to the target prediction state specifically includes: Determine whether there is a recorded track for the tracking target corresponding to the pulse echo data; If it exists, the detection threshold is calculated according to the parameters in the detection probability corresponding to the power fluctuation model of the tracked target and the number of the distance resolution units in the predicted tracking gate; wherein the power fluctuation model is a Weibull distribution model obtained by fitting the recorded track data through a goodness of fit test method; Wherein, the preset formula is as follows: in, is the shape parameter in the detection probability, is the scale parameter in the detection probability, is the number of distance resolution units, is the detection threshold.

2. The method according to claim 1, characterized in that: Generating a target prediction state corresponding to the pulse echo data according to the pulse echo data after sampling and processing, specifically comprising: Determine whether there is a corresponding recorded track for the tracking target; If so, determining the motion state of the tracking target at the previous moment in the recorded trajectory; Determining the change in maneuvering acceleration at the current moment according to the motion state of the tracking target at the previous moment and the target measurement state at the current moment; Determining the acceleration variance at the current moment according to the maneuvering acceleration change; Based on the acceleration variance, the target predicted state is determined; wherein the target measured state is obtained based on the pulse echo data after sampling processing.

3. The method according to claim 2, characterized in that: Generating a target prediction state corresponding to the pulse echo data according to the pulse echo data after sampling and processing, specifically comprising: According to a reference window of a preset number of frames, the acceleration change at each adjacent moment in the reference window is determined; wherein the acceleration change is the acceleration value of the target prediction state at the corresponding moment; Determine the acceleration changes corresponding to each moment before the previous moment, and generate a set of acceleration changes; Determining a maneuvering frequency weight according to the acceleration change at the previous moment and a maximum value in the acceleration change set; Determining the maneuvering frequency at the current moment according to the maneuvering frequency weight and the maneuvering frequency of the tracked target at the previous moment; According to the acceleration variance at the current moment and the maneuvering frequency at the current moment, an operating state equation for generating the target predicted state at the next moment is determined, so as to determine the target predicted state through the motion state equation.

4. The method according to claim 2, characterized in that: Determining the change in maneuvering acceleration at the current moment specifically includes: Determine the target prediction state at the current moment according to the motion state of the tracking target at the previous moment, and generate a one-step prediction function corresponding to the target prediction state; Determine the target measurement state at the current moment according to the pulse echo data after sampling and processing, and generate a measurement function corresponding to the target measurement state; The average of the differences between the measurement function and the one-step prediction function is used as the maneuvering acceleration change at the current moment.

5. The method according to claim 1, characterized in that: After determining the tracking motion state of the tracking target at the next moment, the method further includes: Determine the error between the tracking motion state of the tracking target and the actual motion state of the tracking target within a preset time as a successful tracking rate; When the success tracking rate is less than a preset threshold, generating tracking prompt information; the tracking prompt information includes sound and text; The parameters of the preset formula are adjusted according to the feedback information sent by the user based on the tracking prompt information.

6. The method according to claim 1, characterized in that: The method further comprises: If the recorded track does not exist, the pulse echo data is used as the starting track data of the tracked target; The detection threshold within the predicted tracking gate in the preset detection threshold table is determined according to the pulse echo power of the initial track data.

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