A clutter suppression and angle estimation method and device based on trajectory filtering

Abstract

The present specification discloses a method and device for clutter suppression and angle estimation based on trajectory filtering, which relates to the field of high-power microwave pulse signal detection. It includes: performing two-dimensional constant false alarm rate detection processing on a single pulse target echo signal to obtain measurement values ​​of multiple target trajectories, the measurement values ​​include angle, velocity and acceleration in azimuth, angle, velocity and acceleration in pitch; based on the measurement values ​​of multiple target trajectories, performing trajectory filtering processing on multiple target trajectories respectively to obtain predicted values ​​of target trajectories; based on the measurement values ​​and predicted values ​​of target trajectories at multiple consecutive moments, performing target trajectory management, eliminating false target trajectories, and obtaining target trajectories after trajectory filtering. It solves the problem that the existing clutter suppression method cannot achieve clutter suppression through azimuth angle measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of high-power microwave pulse signal detection, and in particular to a method and device for clutter suppression and angle estimation based on trajectory filtering. Background Art

[0002] High-power microwave pulse signal is a low-repetition-rate extremely narrow pulse signal with an unstable carrier frequency and a nanosecond pulse width, which has a natural high-resolution capability for distance. Therefore, the single pulse amplitude comparison angle measurement can be performed according to the echo amplitude at the distance position of the target, so as to obtain the pitch angle and azimuth angle of the target, and then the distance of the target can be obtained according to the high-resolution capability of the distance, and the relative position of the target to the radar can be determined. However, due to the presence of clutter, errors will occur in the angle measurement and distance measurement of the target, resulting in deviations in target tracking.

[0003] The traditional clutter suppression method is to use moving target detection (MTD) to obtain the target azimuth Doppler frequency shift, and then separate the moving target from the stationary target in the Doppler domain to obtain the relative speed of the target, and finally distinguish the clutter and target signal by speed, so as to complete the clutter suppression and achieve stable tracking of the target. However, since the high-power microwave pulse signal is a low-repetition-rate and extremely narrow pulse signal with a nanosecond pulse width and an unstable carrier frequency, the first blind speed is low, resulting in unstable azimuth phase and inability to complete pulse accumulation, and MTD cannot be used for azimuth angle measurement to complete clutter suppression. Summary of the invention

[0004] The present invention aims to provide a method and device for clutter suppression and angle estimation based on trajectory filtering, so as to solve the problem that the existing clutter suppression method cannot achieve clutter suppression through azimuth angle measurement.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] On the one hand, this specification provides a method and device for clutter suppression and angle estimation based on trajectory filtering, including:

[0007] S1, performing two-dimensional constant false alarm rate detection processing on the single pulse target echo signal to obtain measurement values ​​of multiple target trajectories, wherein the measurement values ​​include angle, velocity and acceleration in azimuth, and angle, velocity and acceleration in pitch;

[0008] S2. Based on the measurement values ​​of the multiple target trajectories, the multiple target trajectories are respectively Trajectory filtering is performed to obtain the predicted value of the target trajectory;

[0009] S3, based on the measured values ​​and predicted values ​​of the target trajectory at multiple consecutive moments, performing target trajectory management, eliminating false target trajectories, and obtaining the target trajectory after trajectory filtering;

[0010] S31, matching the measured value of the target trajectory at the current moment with the predicted value, and determining whether the data of the target trajectory at the current moment matches; wherein the matching step includes: determining whether the difference between the measured value and the predicted value of the target trajectory at the current moment exceeds a preset threshold; if the difference between the two does not exceed the preset threshold, it indicates that the data of the target trajectory at the current moment matches; if the difference between the two exceeds the preset threshold, it indicates that the data of the target trajectory at the current moment does not match;

[0011] S32, if the data of the target trajectory at the current moment does not match, then matching the measured values ​​of the target trajectory at the next multiple consecutive moments with the predicted values ​​to determine whether the data of the target trajectory at the next multiple consecutive moments match;

[0012] S33, if the data of the target trajectory at multiple consecutive moments do not match, the target trajectory is determined to be a false trajectory and is discarded;

[0013] S34: If the data of the target trajectory at the current moment and the next multiple consecutive moments are matched, it is determined that the target trajectory is the target trajectory after trajectory filtering.

[0014] On the other hand, this specification also provides a method and device for clutter suppression and angle estimation based on trajectory filtering, including:

[0015] A target trajectory measurement module is used to perform two-dimensional constant false alarm rate detection processing on a single pulse target echo signal to obtain measurement values ​​of multiple target trajectories, wherein the measurement values ​​include angles, velocities and accelerations in azimuth, and angles, velocities and accelerations in pitch;

[0016] A target trajectory filtering module is used to filter the multiple target trajectories based on the measured values ​​of the multiple target trajectories. Trajectory filtering is performed to obtain the predicted value of the target trajectory;

[0017] The target trajectory management module is used to manage the target trajectory based on the measured values ​​and predicted values ​​of the target trajectory at multiple consecutive moments, eliminate false target trajectories, and obtain the target trajectory after trajectory filtering, specifically including:

[0018] The first matching submodule is used to match the measured value of the target trajectory at the current moment with the predicted value, and determine whether the data of the target trajectory at the current moment matches; wherein the matching step includes: determining whether the difference between the measured value and the predicted value of the target trajectory at the current moment exceeds a preset threshold; if the difference between the two does not exceed the preset threshold, it indicates that the data of the target trajectory at the current moment matches; if the difference between the two exceeds the preset threshold, it indicates that the data of the target trajectory at the current moment does not match;

[0019] The second matching submodule is used to match the measured values ​​of the target trajectory at the next multiple consecutive moments with the predicted values ​​if the data of the target trajectory at the current moment does not match, and determine whether the data of the target trajectory at the next multiple consecutive moments match;

[0020] A false track elimination submodule is used to determine that the target track is a false track and eliminate the target track if the data of the target track at multiple consecutive moments do not match.

[0021] The target trajectory determination submodule is used to determine that the target trajectory is the target trajectory after trajectory filtering if the data of the target trajectory at the current moment and the next multiple consecutive moments are matched.

[0022] On the other hand, this specification also provides an electronic device, including:

[0023] A signal processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the steps of the method as claimed in any one of the preceding claims.

[0024] Based on the above technical solution, this specification can achieve the following technical effects:

[0025] The present invention adopts The trajectory filtering algorithm combines the azimuth and elevation measurement values ​​to predict the target's motion state, and to a certain extent eliminates the trajectory deviation caused by angle measurement and distance measurement, making the target measurement trajectory smoother and reducing the trajectory deviation caused by measurement errors; the target trajectory management method is used to effectively remove some false targets. This realizes the angle prediction and clutter suppression of high-power microwave pulse signals with unstable carrier frequency, and solves the technical problem of clutter interference when using high-power microwave pulse signals for detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a clutter suppression and angle estimation method and device based on trajectory filtering provided in an embodiment of this specification;

[0027] Figure 2 is a schematic diagram of a target trajectory management process provided by an embodiment of this specification;

[0028] Figure 3 is a schematic diagram of the simulation results of the azimuth and elevation angle measurement errors provided in the embodiments of this specification;

[0029] Figure 4 It is a schematic diagram of a clutter suppression and angle estimation method and device based on trajectory filtering provided in an embodiment of this specification. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and claims. It should be noted that the drawings are all in very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0031] It should be noted that, in order to clearly explain the content of the present invention, the present invention specifically cites multiple embodiments to further illustrate different implementations of the present invention, wherein the multiple embodiments are enumerated rather than exhaustive. In addition, for the sake of brevity of explanation, the contents mentioned in the previous embodiments are often omitted in the subsequent embodiments. Therefore, the contents not mentioned in the subsequent embodiments can refer to the previous embodiments accordingly. Example 1

[0032] Please refer to Figure 1 , Figure 1 The figure shows a flow chart of a clutter suppression and angle estimation method and device based on trajectory filtering provided by this embodiment. The method specifically comprises the following steps:

[0033] S1. Perform two-dimensional constant false alarm rate detection processing on the single-pulse target echo signal to obtain measurement values ​​of multiple target trajectories, wherein the measurement values ​​include angles, velocities and accelerations in azimuth and angles, velocities and accelerations in pitch.

[0034] It should be noted that one implementation of S1 may be:

[0035] The two-dimensional constant false alarm rate (CFAR) is used to detect the target on the channel echo data, and the amplitude of the target signal is detected. Then, the position of the target trajectory is measured by the amplitude ratio angle measurement method. The position information includes the angle, velocity and acceleration of the target in azimuth, the angle, velocity and acceleration in pitch, etc. The measured target trajectory is a plurality of target trajectories containing clutter.

[0036] S2. Based on the measurement values ​​of the multiple target trajectories, the multiple target trajectories are respectively Trajectory filtering is performed to obtain the predicted value of the target trajectory.

[0037] It should be noted that one implementation of S2 may be:

[0038] use The filtering algorithm performs trajectory filtering. The input parameters include the measured values ​​of azimuth angle, azimuth velocity, azimuth acceleration, pitch angle, pitch velocity, pitch acceleration, etc. After trajectory filtering, the predicted angle value of the change is obtained, so that the target movement direction can be estimated. The specific steps include:

[0039] S21. Preset Smoothing filter factor for trajectory filtering , where the smoothing filter factor The value range is 0~1;

[0040] S22, according to the value of the smoothing filter factor, determine The value of The following relations are satisfied:

[0041]

[0042] In the formula, when When , it means no smoothing filtering is performed. At this time, the predicted value and the measured value completely coincide with each other, and the measured value is fully believed. When , it indicates that the smoothing filter is increased to ensure the smoothness of the curve and ignore the large deviation of the measured value. At this time, the maneuvering tracking performance of the detection equipment is greatly reduced. is a fixed coefficient value, which is determined by the smoothing filter factor The value of is determined.

[0043] S23, Preset The initial value of the trajectory filter is:

[0044]

[0045] In the formula, is the optimal smoothing value at the first moment, is the velocity smoothing value at the first moment, is the acceleration smoothing value at the first moment, is the velocity smoothing value at the second moment, is the acceleration smoothing value at the second moment, is the predicted value of the target trajectory at the first moment, is the velocity smoothing value at the third moment, , and are the measured values ​​of the target trajectory at the first moment, the second moment, and the third moment, respectively. is the signal processing cycle.

[0046] S24, calculated by the following formula, The predicted value of the target trajectory at time:

[0047]

[0048] In the formula, for The optimal smoothing value at time, for Predicted at any time The predicted value of the target trajectory at time , for The speed smoothing value at the moment, for The speed smoothing value at the moment, for The acceleration smoothing value at the moment, for The acceleration smoothing value at the moment, for Predicted at any time The predicted value of the target trajectory at time , is a fixed value, is the signal processing cycle.

[0049] S3. Based on the measured values ​​and predicted values ​​of the target trajectory at multiple consecutive moments, target trajectory management is performed to eliminate false target trajectories and obtain the target trajectory after trajectory filtering.

[0050] It should be noted that one implementation of S3 may be:

[0051] See also Figure 2 Flow chart of target trajectory management. The specific implementation process is as follows:

[0052] S31, matching the measured value of the target trajectory at the current moment with the predicted value, and judging whether the data of the target trajectory at the current moment matches. Specifically, judging whether the difference between the measured value and the predicted value of the target trajectory at the current moment exceeds a preset threshold value, if the difference between the two does not exceed the preset threshold value, it indicates that the measured value of the target trajectory at the current moment matches the predicted value, if the difference between the two exceeds the preset threshold value, it indicates that the measured value of the target trajectory at the current moment does not match the predicted value;

[0053] S32, if the data of the target trajectory at the current moment does not match, then match the measured values ​​of the target trajectory at the next multiple consecutive moments with the predicted values ​​to determine whether the data of the target trajectory at the next multiple consecutive moments match. Specifically, determine whether the difference between the measured value and the predicted value of the target trajectory at the next multiple consecutive moments exceeds a preset threshold. If the difference between the two does not exceed the preset threshold, it indicates that the measured value of the target trajectory at the next multiple consecutive moments matches the predicted value. If there is a moment when the difference between the measured value and the predicted value of the target trajectory exceeds the preset threshold, it indicates that the measured value of the target trajectory at the next multiple consecutive moments does not match the predicted value.

[0054] S33, if the data of the target trajectory at multiple consecutive moments do not match, the target trajectory is determined to be a false trajectory and is discarded;

[0055] Furthermore, if the data of the target trajectory at multiple consecutive moments match, it is determined that a new target trajectory is obtained, and the process returns to step S31 to match the measured value of the target trajectory at the current moment of the new target trajectory with the predicted value to determine whether the data of the target trajectory at the current moment matches.

[0056] S34: If the data of the target trajectory at the current moment and the next multiple consecutive moments are matched, it is determined that the target trajectory is the target trajectory after trajectory filtering.

[0057] Furthermore, before executing step S31, it is determined whether the signal of the target trajectory at the current moment is lost. Specifically, if the target signal data is not detected for the target trajectory at the current moment, it indicates that the signal of the target trajectory at the current moment is lost; if the signal of the target trajectory at the current moment is lost, the predicted value of the target trajectory at the current moment is returned to step S31 as the measured value of the target trajectory at the current moment, and the number of losses is recorded plus 1; it is determined whether the number of losses of the target trajectory exceeds 3 times; if it exceeds 3 times, it is determined that the target trajectory is lost and the target trajectory is eliminated.

[0058] Based on this, by managing the trajectories of multiple targets (including clutter) after CFAR and eliminating the trajectories of non-real targets, clutter can be suppressed to a certain extent.

[0059] This embodiment simulates the azimuth and elevation angle measurement errors. The simulation results are as follows: Figure 3 As shown, it can be seen that the angle measurement errors in azimuth and elevation are small, that is, a better angle measurement value can be obtained through the trajectory filtering algorithm, completing the angle prediction and trajectory filtering of the target.

[0060] In summary, this method adopts The trajectory filtering algorithm combines the azimuth and elevation measurement values ​​to predict the target's motion state, and to a certain extent eliminates the trajectory deviation caused by angle measurement and distance measurement, making the target measurement trajectory smoother and reducing the trajectory deviation caused by measurement errors; the target trajectory management method is used to effectively remove some false targets. This realizes the angle prediction and clutter suppression of high-power microwave pulse signals with unstable carrier frequencies. It solves the technical problem of clutter interference when using high-power microwave pulse signals for detection. Example 2

[0061] This embodiment provides another implementation of a clutter suppression and angle estimation method and device based on trajectory filtering, which specifically includes:

[0062] In a single pulse angle measurement process, the target amplitude is first detected using CFAR technology, and then the target angle information is obtained using a single pulse tracking algorithm. The spatial position of the target is then inferred by combining the inertial navigation data and the antenna beam pointing. The next angle measurement process is similar. By matching each target point in the previous and subsequent angle measurement processes, combined with the point distance and the wave gate, the optimal point-to-point matching solution is obtained. At the same time, track management of multiple targets (including clutter) after CFAR can suppress clutter to a certain extent. The track management process is as follows: Figure 2 The specific implementation process is as follows:

[0063] If the existing target track is lost, that is, there is no echo signal, then a target position is extrapolated using the track filtering algorithm as the position of the track target at this time, and the number of times the track target is lost is recorded, and so on; if the target is lost in three consecutive angle measurement processes, the track target is deleted and it is judged that the target is lost;

[0064] If the angle measurement data of this time does not match the angle measurement data predicted last time and the subsequent angle measurement data all match, it is judged that a new track target appears, and a new track target is opened;

[0065] If the angle measurement data of this time does not match the angle measurement data predicted last time and the subsequent angle measurement data do not match, then the trajectory is judged to be a false trajectory and the trajectory target is deleted.

[0066] The trajectory management algorithm is implemented based on the trajectory filtering algorithm. Filtering is the core part of trajectory filtering algorithm. The input parameters of the trajectory filtering algorithm include: azimuth angle, azimuth velocity, azimuth acceleration, pitch angle, pitch velocity, pitch acceleration and other measurement values. After trajectory filtering, the predicted angle value of the change is obtained, so that the target movement direction can be estimated, and false targets can be eliminated to achieve anti-interference. The specific implementation process is as follows:

[0067] Assume the target measurement value is The angle prediction value at the next moment is The optimal smoothing value is , then in Time position smoothing value , speed smoothing value And the acceleration smoothing value They are:

[0068]

[0069] The predicted value at the moment is:

[0070] According to the above formula Trajectory filter, its initial value is:

[0071]

[0072] in, is the signal processing cycle, The choice of affects the performance of the filter, and its value is fixed. is the smoothing filter factor, then The following relationship is satisfied:

[0073]

[0074] In the formula, when When , it means no smoothing filtering is performed. At this time, the predicted value and the measured value completely coincide with each other, and the measured value is fully believed. When the smoothing filter is increased, the curve is smoothed and the large deviation of the measured value is ignored. At this time, the maneuvering tracking performance of the detection equipment is greatly reduced. By selecting the appropriate smoothing filter factor ,conduct Trajectory filtering can eliminate false targets and ultimately achieve target velocity estimation and clutter suppression.

[0075] At the same time, after obtaining the target filtered position, the target angle can be recalculated according to the smoothed position value to obtain a better angle measurement value, thereby completing the target trajectory filtering and angle tracking. Example 3

[0076] Please refer to Figure 4 , Figure 4 The figure shows a schematic diagram of a clutter suppression and angle estimation method and device based on trajectory filtering provided by this embodiment. The device:

[0077] The target trajectory measurement module 301 is used to perform two-dimensional constant false alarm rate detection processing on the single pulse target echo signal to obtain measurement values ​​of multiple target trajectories, wherein the measurement values ​​include angle, velocity and acceleration in azimuth, and angle, velocity and acceleration in pitch;

[0078] The target trajectory filtering module 302 is used to filter the multiple target trajectories based on the measured values ​​of the multiple target trajectories. Trajectory filtering is performed to obtain the predicted value of the target trajectory;

[0079] The target trajectory management module 303 is used to manage the target trajectory based on the measured values ​​and predicted values ​​of the target trajectory at multiple consecutive moments, remove false target trajectories, and obtain the target trajectory after trajectory filtering, specifically including:

[0080] The first matching submodule is used to match the measured value of the target trajectory at the current moment with the predicted value, and determine whether the data of the target trajectory at the current moment matches; wherein the matching step includes: determining whether the difference between the measured value and the predicted value of the target trajectory at the current moment exceeds a preset threshold; if the difference between the two does not exceed the preset threshold, it indicates that the data of the target trajectory at the current moment matches; if the difference between the two exceeds the preset threshold, it indicates that the data of the target trajectory at the current moment does not match;

[0081] The second matching submodule is used to match the measured values ​​of the target trajectory at the next multiple consecutive moments with the predicted values ​​if the data of the target trajectory at the current moment does not match, and determine whether the data of the target trajectory at the next multiple consecutive moments match;

[0082] A false track elimination submodule is used to determine that the target track is a false track and eliminate the target track if the data of the target track at multiple consecutive moments do not match.

[0083] The target trajectory determination submodule is used to determine that the target trajectory is the target trajectory after trajectory filtering if the data of the target trajectory at the current moment and the next multiple consecutive moments are matched.

[0084] In summary, this method adopts The trajectory filtering algorithm combines the azimuth and elevation measurement values ​​to predict the target's motion state, and to a certain extent eliminates the trajectory deviation caused by angle measurement and distance measurement, making the target measurement trajectory smoother and reducing the trajectory deviation caused by measurement errors; the target trajectory management method is used to effectively remove some false targets. This realizes the angle prediction and clutter suppression of high-power microwave pulse signals with unstable carrier frequencies. It solves the technical problem of clutter interference when using high-power microwave pulse signals for detection. Example 4

[0085] In another feasible embodiment, this embodiment provides a device for clutter suppression and angle estimation based on trajectory filtering, and the device may specifically include:

[0086] Processor; and

[0087] A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the steps in any of the above method embodiments. Example 5

[0088] In another feasible embodiment, this embodiment provides a storage medium for clutter suppression and angle estimation based on trajectory filtering, and the storage medium may specifically include:

[0089] The storage medium stores a processing program for clutter suppression and angle estimation based on trajectory filtering, and when the processing program for clutter suppression and angle estimation based on trajectory filtering is executed by a processor, the steps in any of the above method embodiments are implemented.

[0090] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification can be modified and varied in various ways. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A clutter suppression and angle estimation method based on trajectory filtering, characterized in that: include: S1, performing two-dimensional constant false alarm rate detection processing on the single pulse target echo signal to obtain measurement values ​​of multiple target trajectories, wherein the measurement values ​​include angle, velocity and acceleration in azimuth, and angle, velocity and acceleration in pitch; S2. Based on the measured values ​​of the multiple target trajectories, perform α-β-γ trajectory filtering on the multiple target trajectories to obtain predicted values ​​of the target trajectories. The specific steps include: S21, presetting a smoothing filter factor ζ of the α-β-γ trajectory filter, wherein the value range of the smoothing filter factor ζ is 0 to 1; S22. Determine the values ​​of α, β, and γ according to the value of the smoothing filter factor, where α, β, and γ satisfy the following relationship: S23, presetting the initial value of the α-β-γ trajectory filter, the initial value includes the optimal smoothing value X of the position at the first moment s (1) Speed ​​smoothing value Acceleration smoothing value The speed smoothing value at the second moment Acceleration smoothing value And the predicted value X of the target trajectory p (2) Speed ​​smoothing value at the third moment Specifically include: X s (1)=X p (2)=X0(1) Where X0(1), X0(2) and X0(3) are the measured values ​​of the target trajectory at the first moment, the second moment and the third moment respectively; S24, according to the measured value of the target trajectory at the current moment and the predicted value of the target trajectory at the current moment predicted at the previous moment, calculate the optimal smoothed value of the position at the current moment; according to the measured value of the target trajectory at the current moment, the predicted value of the target trajectory at the current moment predicted at the previous moment and the smoothed value of the speed at the previous moment, calculate the smoothed value of the speed at the current moment; according to the measured value of the target trajectory at the current moment, the predicted value of the target trajectory at the current moment predicted at the previous moment and the smoothed value of the acceleration at the previous moment, calculate the smoothed value of the acceleration at the current moment; according to the optimal smoothed value of the position at the current moment, the smoothed value of the speed at the current moment and the smoothed value of the acceleration at the current moment, calculate the predicted value of the target trajectory at the next moment; specifically, the calculation is performed by the following formula: X s (n)=X p (n)+α(X0(n)-X p (n))X0(n) Where X0(n) is the measured value of the target trajectory at time n, X s (n) is the optimal smoothing value of the position at time n, X p (n) is the predicted value of the target trajectory at time n obtained by predicting at time n-1, is the speed smoothing value at time n, is the speed smoothing value at time n-1, is the acceleration smoothing value at time n, is the acceleration smoothing value at time n-1, X p (n+1) is the predicted value of the target trajectory at time n+1 obtained by prediction at time n, α, β, γ are fixed values, and T is the signal processing cycle; S3, based on the measured values ​​and predicted values ​​of the target trajectory at multiple consecutive moments, target trajectory management is performed to eliminate false target trajectories and obtain the target trajectory after trajectory filtering. The specific steps include: S31, matching the measured value of the target trajectory at the current moment with the predicted value, and determining whether the data of the target trajectory at the current moment matches; wherein the matching step includes: determining whether the difference between the measured value and the predicted value of the target trajectory at the current moment exceeds a preset threshold; if the difference between the two does not exceed the preset threshold, it indicates that the data of the target trajectory at the current moment matches; if the difference between the two exceeds the preset threshold, it indicates that the data of the target trajectory at the current moment does not match; S32, if the data of the target trajectory at the current moment does not match, then matching the measured values ​​of the target trajectory at the next multiple consecutive moments with the predicted values ​​to determine whether the data of the target trajectory at the next multiple consecutive moments match; S33, if the data of the target trajectory at multiple consecutive moments do not match, the target trajectory is determined to be a false trajectory and is discarded; S34: If the data of the target trajectory at the current moment and the next multiple consecutive moments are matched, it is determined that the target trajectory is the target trajectory after trajectory filtering.

2. The method according to claim 1, characterized in that After matching the position of the target trajectory at the next multiple consecutive moments with the predicted value at the corresponding moment and determining whether the data of the target trajectory at the next multiple consecutive moments match, the method further includes: If the data of the target trajectory at multiple consecutive moments matches, it is determined that a new target trajectory is obtained, and the process returns to step S31.

3. The method according to claim 1, characterized in that Before executing step S31, the method further includes: Determine whether the signal of the target trajectory at the current moment is lost; If the signal of the target track at the current moment is lost, the predicted value of the target track at the current moment is returned to step S31 as the measured value of the target track at the current moment, and the number of times of loss is recorded plus 1; Determining whether the number of losses exceeds a preset threshold; If it exceeds the preset threshold, the target track is determined to be lost and is removed.

4. A device using the clutter suppression and angle estimation method based on trajectory filtering according to claim 1, characterized in that: include: A target trajectory measurement module is used to perform two-dimensional constant false alarm rate detection processing on a single pulse target echo signal to obtain measurement values ​​of multiple target trajectories, wherein the measurement values ​​include angles, velocities and accelerations in azimuth, and angles, velocities and accelerations in pitch; The target trajectory filtering module is used to perform α-β-γ trajectory filtering processing on the multiple target trajectories based on the measured values ​​of the multiple target trajectories to obtain the predicted values ​​of the target trajectories. The specific steps include: S21, presetting a smoothing filter factor ζ of the α-β-γ trajectory filter, wherein the value range of the smoothing filter factor ζ is 0 to 1; S22. Determine the values ​​of α, β, and γ according to the value of the smoothing filter factor, where α, β, and γ satisfy the following relationship: S23, presetting the initial value of the α-β-γ trajectory filter, the initial value includes the optimal smoothing value X of the position at the first moment s (1) Speed ​​smoothing value Acceleration smoothing value The speed smoothing value at the second moment Acceleration smoothing value And the predicted value X of the target trajectory p (2) Speed ​​smoothing value at the third moment Specifically include: X s (1)=X p (2)=X0(1) Where X0(1), X0(2) and X0(3) are the measured values ​​of the target trajectory at the first moment, the second moment and the third moment respectively; S24, according to the measured value of the target trajectory at the current moment and the predicted value of the target trajectory at the current moment predicted at the previous moment, calculate the optimal smoothed value of the position at the current moment; according to the measured value of the target trajectory at the current moment, the predicted value of the target trajectory at the current moment predicted at the previous moment and the smoothed value of the speed at the previous moment, calculate the smoothed value of the speed at the current moment; according to the measured value of the target trajectory at the current moment, the predicted value of the target trajectory at the current moment predicted at the previous moment and the smoothed value of the acceleration at the previous moment, calculate the smoothed value of the acceleration at the current moment; according to the optimal smoothed value of the position at the current moment, the smoothed value of the speed at the current moment and the smoothed value of the acceleration at the current moment, calculate the predicted value of the target trajectory at the next moment; specifically, the calculation is performed by the following formula: X s (n)=X p (n)+α(X0(n)-X p (n))X0(n) Where X0(n) is the measured value of the target trajectory at time n, X s (n) is the optimal smoothing value of the position at time n, X p (n) is the predicted value of the target trajectory at time n obtained by predicting at time n-1, is the speed smoothing value at time n, is the speed smoothing value at time n-1, is the acceleration smoothing value at time n, is the acceleration smoothing value at time n-1, X p (n+1) is the predicted value of the target trajectory at time n+1 obtained by prediction at time n, α, β, γ are fixed values, and t is the signal processing cycle; The target trajectory management module is used to manage the target trajectory based on the measured values ​​and predicted values ​​of the target trajectory at multiple consecutive moments, eliminate false target trajectories, and obtain the target trajectory after trajectory filtering, specifically including: The first matching submodule is used to match the measured value of the target trajectory at the current moment with the predicted value, and determine whether the data of the target trajectory at the current moment matches; wherein the matching step includes: determining whether the difference between the measured value and the predicted value of the target trajectory at the current moment exceeds a preset threshold; if the difference between the two does not exceed the preset threshold, it indicates that the data of the target trajectory at the current moment matches; if the difference between the two exceeds the preset threshold, it indicates that the data of the target trajectory at the current moment does not match; The second matching submodule is used to match the measured values ​​of the target trajectory at the next multiple consecutive moments with the predicted values ​​if the data of the target trajectory at the current moment does not match, and determine whether the data of the target trajectory at the next multiple consecutive moments match; A false track elimination submodule is used to determine that the target track is a false track and eliminate the target track if the data of the target track at multiple consecutive moments do not match. The target trajectory determination submodule is used to determine that the target trajectory is the target trajectory after trajectory filtering if the data of the target trajectory at the current moment and the next multiple consecutive moments are matched.

5. An electronic device, characterized in that: include: Signal processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 3.

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