High-speed target motion track re-estimation method and system based on compact high-frequency radar

Abstract

The present invention provides a method and system for re-estimating the track of high-speed target motion based on a compact high-frequency radar, belonging to the field of radar technology. The method comprises: performing coherent accumulation of original radar echo data over a preset time period to obtain a target radial distance value and a target velocity value, and determining a target arrival angle value through a multiple signal classification algorithm; calculating the target original point track based on the target radial distance value and the target arrival angle value, and performing track prediction on the target original point track using the target velocity value to obtain a predicted track set; calculating the average heading of each predicted track in the predicted track set, filtering the predicted track set according to a heading threshold to obtain a filtered predicted track set; and fusing the filtered predicted track set to obtain a final track result. The present invention uses velocity measurement values to re-estimate the target original point track, effectively improving the track accuracy output by the compact high-frequency radar without changing the arrival angle estimation accuracy.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a method and system for re-estimating the track of a high-speed target motion based on a compact high-frequency radar. Background Art

[0002] High-frequency (HF) radar, with its capabilities for beyond-horizon detection and all-weather operation, is attracting increasing attention, particularly in target tracking and positioning. Among these applications, compact high-frequency surface wave radar (HFSWR) is widely used in coastal areas due to its small installation footprint, ease of maintenance, and low cost. In recent years, HFSWR has played a significant role in sea state inversion and slow-moving target detection.

[0003] The conventional approach to track measurement with HFSWR is to detect first and then track. First, constant false alarm rate (CFAR) detection is performed on the range-Doppler spectrum to determine the target's radial range and radial velocity. The multiple signal classification (MUSIC) method is then used to estimate the direction of arrival (DOA) of the target signal from the cross-loop / monopole antenna, thereby obtaining the target's raw track. Finally, the raw track is filtered using a filtering algorithm such as a Kalman filter to output the final track. However, due to the large error in DOA measurements, typically reaching around 10°, filtering algorithms do not significantly improve the accuracy of the output track. Therefore, many researchers are working to improve DOA estimation accuracy. For example, the TF-Music algorithm extracts envelope information from the target signal along the time-frequency ridge, eliminating signal non-stationarity caused by Doppler frequency variations, thereby improving the heading accuracy of non-stationary targets. Furthermore, using a binary cross-loop / monopole antenna array for angle estimation can achieve a narrower beamwidth, thereby improving DOA estimation accuracy. However, these two methods have little effect on improving DOA estimation accuracy when the target signal-to-noise ratio is low. Furthermore, cross-localization using bistatic radars can improve target positioning accuracy, but this requires expensive hardware support. In summary, outputting high-precision motion tracks for high-speed targets using HFSWR presents a significant challenge.

[0004] To address the challenges in the above applications, it is necessary to propose new methods for high-speed target motion trajectory tracking and estimation. Summary of the Invention

[0005] The present invention provides a high-speed target motion track re-estimation method and system based on a compact high-frequency radar, which is used to solve the defect of low accuracy in high-speed target motion track estimation in the prior art using compact high-frequency radar.

[0006] In a first aspect, the present invention provides a method for re-estimating the track of a high-speed target motion based on a compact high-frequency radar, comprising:

[0007] Receive raw radar echo data, perform a long-term coherent integration algorithm on the raw radar echo data for a preset time period to obtain a target radial distance value and a target speed value, and determine a target arrival angle value through a multiple signal classification algorithm;

[0008] Calculate the target original track according to the target radial distance value and the target arrival angle value, and use the target speed value to perform track prediction on the target original track to obtain a predicted track set;

[0009] Calculating an average heading of each predicted track in the predicted track set, and filtering the predicted track set according to a heading threshold to obtain a filtered predicted track set;

[0010] The filtered predicted track set is integrated to obtain the final track result.

[0011] In a second aspect, the present invention further provides a high-speed target motion track re-estimation system based on a compact high-frequency radar, comprising:

[0012] a detection module, configured to receive raw radar echo data, perform a long-term coherent integration algorithm on the raw radar echo data for a preset time period to obtain a target radial distance value and a target velocity value, and determine a target arrival angle value through a multiple signal classification algorithm;

[0013] a calculation module, configured to calculate a target original track according to the target radial distance value and the target arrival angle value, and perform track prediction on the target original track using the target speed value to obtain a predicted track set;

[0014] a screening module, configured to calculate an average heading of each predicted track in the predicted track set, and screen the predicted track set according to a heading threshold to obtain a screened predicted track set;

[0015] The fusion module is used to fuse the filtered predicted track set to obtain the final track result.

[0016] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the high-speed target motion track re-estimation method based on a compact high-frequency radar as described above is implemented.

[0017] The present invention provides a method and system for re-estimating the motion track of a high-speed target based on a compact high-frequency radar. By fully utilizing the correlation between radar measurement data, the target's velocity measurement and original point track are used to predict the target's motion track, and the velocity measurement value is used to predict the target's original point track. This effectively improves the output track accuracy of the compact high-frequency radar without changing the arrival angle estimation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a flow chart of a high-speed target motion track re-estimation method based on a compact high-frequency radar provided by the present invention;

[0020] Figure 2 Schematic diagram of the DOA measurement results provided by the present invention;

[0021] Figure 3 is a statistical histogram of the predicted track corresponding to the heading provided by the present invention;

[0022] Figure 4 Schematic diagram of the re-estimated motion track result provided by the present invention;

[0023] Figure 5 This is a schematic structural diagram of a high-speed target motion track re-estimation system based on a compact high-frequency radar provided by the present invention;

[0024] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0026] When existing high-frequency compact radars detect high-speed targets, due to the high speed of the target, the radial distance between the target and the radar changes rapidly, the radar data needs to estimate multiple DOAs, and the number of signal snapshots is small, resulting in the measured track deviating from the actual track. The present invention proposes a high-speed target motion track re-estimation method based on a compact target radar, which can accurately estimate the motion track of low-altitude flying targets without changing the radar waveform.

[0027] Figure 1 FIG is a flow chart of a method for re-estimating a high-speed target motion track based on a compact high-frequency radar according to an embodiment of the present invention. Figure 1 As shown, including:

[0028] Step 100: Receive raw radar echo data, perform a long-term coherent integration algorithm on the raw radar echo data for a preset time period to obtain a target radial distance value and a target velocity value, and determine a target arrival angle value using a multiple signal classification algorithm;

[0029] Step 200: Calculating a target original track based on the target radial distance value and the target arrival angle value, and performing track prediction on the target original track using the target speed value to obtain a predicted track set;

[0030] Step 300: Calculate the average heading of each predicted track in the predicted track set, and filter the predicted track set according to a heading threshold to obtain a filtered predicted track set;

[0031] Step 400: Fusing the filtered predicted track set to obtain a final track result.

[0032] Specifically, after receiving radar echo data, the embodiment of the present invention performs long-term coherent accumulation detection on the original radar data to obtain the radial distance and speed measurement values of the target, and estimates the corresponding DOA change through the MUSIC algorithm; calculates the original point track of the target based on the radial distance and DOA measurement value of the target, and predicts the track of the original point track in combination with the speed measurement value; calculates the average heading of each predicted track, and uses the heading threshold to filter the predicted track set; fuses the filtered predicted track set, completes the correction of the original point track, and outputs the final track result.

[0033] The present invention makes full use of the correlation between radar measurement data and uses the velocity measurement value to re-estimate the original target track, effectively improving the output track accuracy of the compact high-frequency radar without changing the arrival angle estimation accuracy.

[0034] Based on the above embodiment, the raw radar echo data is coherently integrated for a preset time period to obtain a target radial distance value and a target velocity value, and a target arrival angle value is determined by a multiple signal classification algorithm, including:

[0035] Determine the total number of radar sweep cycles, obtain the radial distance value and velocity value of any sweep cycle in the total number of radar sweep cycles through a long-term coherent integration algorithm in a preset time period, and obtain the arrival angle value of any sweep cycle in the total number of radar sweep cycles through a multiple signal classification algorithm.

[0036] Specifically, the embodiment of the present invention performs long-term coherent integration and MUSIC algorithm estimation on the original radar data to obtain the radial range, speed, and DOA measurement values of the target, which are:

[0037]

[0038]

[0039]

[0040] Among them, r m 、v m ,θ m They represent the true radial distance, true velocity and true DOA of the target in the mth sweep cycle respectively; ε m,r , ε m,v , ε m,θ They represent the measurement errors of the radar corresponding to radial distance, velocity value and DOA respectively; m=1, 2, ..., N, where N represents the total number of radar sweep cycles, and in the embodiment of the present invention, N=256.

[0041] like Figure 2 From the DOA measurement results shown in FIG, it can be found that due to the existence of measurement errors, there is a certain deviation between the DOA measurement results and the true results.

[0042] Based on the above embodiment, the target original point trace is calculated according to the target radial distance value and the target arrival angle value, including:

[0043] The target original point trace is obtained by multiplying the target radial distance value of any sweep frequency cycle by the sine value and cosine value of the corresponding arrival angle value.

[0044] Specifically, the embodiment of the present invention calculates the original track of the target based on the radial distance and DOA measurement value of the target, and performs track prediction on the original track in combination with the speed measurement value. The expression is:

[0045]

[0046] in, is the original point trace of the measurement, They represent the radial distance, velocity and DOA of the mth sweep cycle measured by the radar respectively.

[0047] Based on the above embodiment, the target original point track is predicted using the target speed value to obtain a predicted track set, including:

[0048] determining the correlation coefficient between the radar measured angle of arrival measurement set and the radar sweep time series;

[0049] Determine the target predicted arrival angle value for the nth sweep cycle based on the target's arrival angle value and radial distance value for the mth sweep cycle, the radial distance value for the nth sweep cycle, the distance difference between the target's movement within the mth sweep cycle and the nth sweep cycle, and the correlation coefficient, where m = 1, 2, ..., N, and n = 1, 2, ..., N, where N is the number of sweep cycles;

[0050] A predicted motion track is obtained by multiplying the target radial distance value of any sweep cycle with the sine and cosine values of the corresponding predicted arrival angle value;

[0051] Repeat the above steps to obtain the predicted track set.

[0052] Determining the correlation coefficient between the radar arrival angle measurement set and the frequency sweep time series includes:

[0053] Determining the radar arrival angle measurement set and the radar frequency sweep time series set;

[0054] Calculating the variance of the arrival angle measurement set to obtain a first variance, calculating the variance of the radar frequency sweep time series set to obtain a second variance, and calculating the covariance of the arrival angle measurement set and the radar frequency sweep time series set;

[0055] The correlation coefficient is obtained according to the covariance, the first variance, and the second variance.

[0056] Wherein, based on the arrival angle value and radial distance value of the target in the mth sweep frequency cycle, the radial distance value of the nth sweep frequency cycle, the distance difference between the target movement in the mth sweep frequency cycle and the nth sweep frequency cycle, and the correlation coefficient, the target predicted arrival angle value of the target in the nth sweep frequency cycle is determined, including:

[0057] Determine the radial distance value and arrival angle value of the target in the mth sweep cycle, the radial distance value of the target in the nth sweep cycle, and the distance difference between the target movement in the mth sweep cycle and the nth sweep cycle;

[0058] Obtaining an angle of arrival adjustment angle based on a radial distance value of the target in the mth frequency sweep cycle, a radial distance value of the target in the nth frequency sweep cycle, and a distance difference between the target movement in the mth frequency sweep cycle and the nth frequency sweep cycle;

[0059] If it is determined that m and n are not equal, and the product of the difference between m and n and the correlation coefficient is less than 0, then the predicted arrival angle value of the target n-th sweep frequency cycle is obtained by adding the arrival angle adjustment angle to the arrival angle value of the target m-th sweep frequency cycle;

[0060] If it is determined that m and n are not equal, and the product of the difference between m and n and the correlation coefficient is greater than 0, then subtracting the arrival angle adjustment angle from the arrival angle value of the target m-th sweep frequency cycle to obtain the predicted arrival angle value of the target n-th sweep frequency cycle;

[0061] If it is determined that m is equal to n, then the target predicted arrival angle value of the target n-th frequency sweep cycle is equal to the arrival angle value of the target m-th frequency sweep cycle.

[0062] Specifically, in the embodiment of the present invention, the original point track is predicted using velocity measurement. The radial distance and DOA of the mth sweep cycle measured by the radar are used as the base point. Combined with the radial distance of the nth sweep cycle measured by the radar, the DOA of the nth sweep cycle is predicted as follows:

[0063]

[0064] in, It represents the DOA of the nth sweep cycle predicted based on the radar measurement value of the mth sweep cycle. is the DOA measured by the radar in the mth sweep cycle. arccos() is the inverse cosine function. is the radial distance measured by the radar in the mth sweep cycle, is the radial distance measured by the radar in the nth sweep cycle, is the speed measured by the radar in the mth sweep cycle, T r is the duration of a frequency sweep cycle. ρ is the DOA and time mT measured by the radar r The correlation coefficient between them is defined as follows:

[0065]

[0066] in, express Covariance with T, express The variance of T, D(T) represents the variance of T, is the DOA measurement set, T={mT r|m=1,2,...,N} is the radar time series set.

[0067] Furthermore, taking the radar measurement value of the mth sweep cycle as the base point, the predicted motion track is

[0068]

[0069] Then, based on the radar measurement value of any sweep cycle, the set of predicted motion tracks is:

[0070]

[0071] Among them, U p is the set of predicted motion tracks.

[0072] Based on the above embodiment, the average heading of each predicted track in the predicted track set is calculated, and the predicted track set is filtered according to the heading threshold to obtain a filtered predicted track set, including:

[0073] Obtain any predicted track in the predicted track set;

[0074] Determine the longitude and latitude of any sweep cycle, the latitude of any sweep cycle, and the longitude and latitude of the next cycle of any sweep cycle for any predicted track;

[0075] Determine a first target position based on the longitude and latitude of any one frequency sweep cycle, and determine a second target position based on the longitude and latitude of a next cycle of any one frequency sweep cycle;

[0076] Calculating the heading from the first target position to the second target position, summing and averaging all headings of any predicted track in the total number of radar sweep cycles to obtain an average heading of any predicted track;

[0077] Repeat the above steps to obtain the set of average headings of all predicted tracks;

[0078] Performing statistical analysis on the set of average headings of each predicted track based on a statistical histogram, obtaining the heading corresponding to the highest box center in the histogram, and determining the proportion of preset track retention based on heading information near the highest box center;

[0079] Determine the lower boundary and the upper boundary of the filtering heading according to the heading corresponding to the highest box center and the preset track retention ratio;

[0080] The set of average headings of all the predicted tracks is filtered using the filtered heading lower boundary and the filtered heading upper boundary to obtain the filtered predicted track set.

[0081] Specifically, the embodiment of the present invention calculates the average heading of each predicted track and uses a heading threshold to filter the predicted track set.

[0082] Here, the average heading of each predicted track is calculated, and its expression is:

[0083]

[0084] Among them, ψ m is the average heading of the mth track, head(P1,P2) represents the heading from P1 to P2, x m,k is the longitude of the kth sweep cycle of the mth track, y m,k is the latitude of the kth sweep cycle of the mth track, x m,k+1 is the longitude of the k+1th sweep cycle of the mth track, y m,k+1 is the latitude of the k+1th sweep cycle of the mth track.

[0085] Next, use the heading information to filter the predicted track. The steps are as follows:

[0086] Perform statistical analysis on the set of headings to obtain its statistical histogram. Assume that the heading corresponding to the highest box center of the histogram is ψ c = 119°. In order to retain the heading information of the highest box and its vicinity of about ξ = 80 percent, the upper and lower boundaries of the heading are defined as:

[0087]

[0088] Among them, χ l Indicates the lower boundary of the screening heading, χ r represents the upper boundary of the screening heading, P{χ l <ψ m < r} indicates the heading is in χ l to χ r The probability between min Indicates the minimum value of heading, ψ max represents the maximum value of the heading, and ξ represents the percentage of the predicted track retained.

[0089] like Figure 3 The predicted track shown corresponds to the statistical histogram of the heading. In order to retain the heading information of about 80 percent (ξ) of the highest box and its vicinity, the lower boundary of the heading is 110° and the upper boundary is 130°.

[0090] Based on the above heading boundaries, the predicted tracks are filtered using their headings:

[0091]

[0092] in, is the heading set of the predicted track, χ l =110° represents the lower boundary of the screening heading, χ r =130° represents the upper boundary of the screening heading, U p is the set of predicted trajectories, U p,f is the set of filtered predicted tracks.

[0093] Based on the above embodiment, the filtered predicted track set is integrated to obtain the final track result, including:

[0094] Based on the total number of radar sweep cycles and the preset track retention ratio, the filtered predicted track set is averaged and fused to obtain the final track result.

[0095] Specifically, the filtered predicted track set is fused to complete the correction of the original track and output the final track result, which is expressed as:

[0096]

[0097] Among them, S p is the fused track.

[0098] Figure 4 The corrected motion track result in the embodiment of the present invention uses different symbols and markings to distinguish the radar position, original point track, true track, and re-estimated track. It can be found that the re-estimated motion track is more accurate than the original radar measurement track.

[0099] The following describes the high-speed target motion track re-estimation system based on compact high-frequency radar provided by the present invention. The high-speed target motion track re-estimation system based on compact high-frequency radar described below and the high-speed target motion track re-estimation method based on compact high-frequency radar described above can be referenced to each other.

[0100] Figure 5 FIG is a schematic diagram of a high-speed target motion track re-estimation system based on a compact high-frequency radar according to an embodiment of the present invention. Figure 5 As shown, it includes: a detection module 51, a calculation module 52, a screening module 53 and a fusion module 54, wherein:

[0101] The detection module 51 is used to receive the original radar echo data, perform a long-term coherent integration algorithm on the original radar echo data for a preset time period to obtain the target radial distance value and the target speed value, and determine the target arrival angle value through a multiple signal classification algorithm; the calculation module 52 is used to calculate the target original point track based on the target radial distance value and the target arrival angle value, and use the target speed value to perform track prediction on the target original point track to obtain a predicted track set; the screening module 53 is used to calculate the average heading of each predicted track in the predicted track set, and screen the predicted track set according to the heading threshold to obtain a screened predicted track set; the fusion module 54 is used to fuse the screened predicted track set to obtain a final track result.

[0102] Figure 6 An example of a physical structure diagram of an electronic device is shown below. Figure 6 As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other via the communications bus 640. The processor 610 may call logic instructions in the memory 630 to execute a method for re-estimating a high-speed target motion track based on a compact high-frequency radar. The method includes: receiving raw radar echo data, performing a long-term coherent integration algorithm for a preset time period on the raw radar echo data to obtain a target radial distance value and a target velocity value, and determining a target arrival angle value using a multiple signal classification algorithm; calculating a target original point track based on the target radial distance value and the target arrival angle value, and performing track prediction on the target original point track using the target velocity value to obtain a predicted track set; calculating the average heading of each predicted track in the predicted track set, filtering the predicted track set according to a heading threshold to obtain a filtered predicted track set; and fusing the filtered predicted track set to obtain a final track result.

[0103] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0104] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the compact high-frequency radar high-speed target motion track re-estimation method provided by the above-mentioned methods. The method includes: receiving original radar echo data, performing a long-term coherent accumulation algorithm for a preset time period on the original radar echo data to obtain a target radial distance value and a target speed value, and determining a target arrival angle value through a multiple signal classification algorithm; calculating the target original point track based on the target radial distance value and the target arrival angle value, and using the target speed value to perform track prediction on the target original point track to obtain a predicted track set; calculating the average heading of each predicted track in the predicted track set, filtering the predicted track set according to a heading threshold to obtain a filtered predicted track set; fusing the filtered predicted track set to obtain a final track result.

[0105] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for re-estimating the track of high-speed target motion based on compact high-frequency radar provided by the above-mentioned methods, the method comprising: receiving original radar echo data, performing a long-term coherent accumulation algorithm for a preset time period on the original radar echo data to obtain a target radial distance value and a target speed value, and determining a target arrival angle value through a multiple signal classification algorithm; calculating the target original point track based on the target radial distance value and the target arrival angle value, and performing track prediction on the target original point track using the target speed value to obtain a predicted track set; calculating the average heading of each predicted track in the predicted track set, filtering the predicted track set according to a heading threshold to obtain a filtered predicted track set; and fusing the filtered predicted track set to obtain a final track result.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0107] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for re-estimating the trajectory of a high-speed target based on a compact high-frequency radar, characterized in that: include: Receive raw radar echo data, perform a long-term coherent integration algorithm on the raw radar echo data for a preset time period to obtain a target radial distance value and a target speed value, and determine a target arrival angle value through a multiple signal classification algorithm; Calculate the target original track according to the target radial distance value and the target arrival angle value, and use the target speed value to perform track prediction on the target original track to obtain a predicted track set; Calculating an average heading of each predicted track in the predicted track set, and filtering the predicted track set according to a heading threshold to obtain a filtered predicted track set; The filtered predicted track set is integrated to obtain the final track result; Calculating the average heading of each predicted track in the predicted track set, filtering the predicted track set according to the heading threshold, and obtaining a filtered predicted track set, including: Obtain any predicted track in the predicted track set; Determining the longitude and latitude of any frequency sweep cycle of any predicted track, and the longitude and latitude of the next cycle of any frequency sweep cycle; Determine a first target position based on the longitude and latitude of any one frequency sweep cycle, and determine a second target position based on the longitude and latitude of a next cycle of any one frequency sweep cycle; Calculating the heading from the first target position to the second target position, summing and averaging all headings of any predicted track in the total number of radar sweep cycles to obtain an average heading of any predicted track; Repeat the above steps to obtain the set of average headings of all predicted tracks; Performing statistical analysis on the set of average headings of the predicted tracks based on a statistical histogram, obtaining the heading corresponding to the highest box center in the histogram, and determining the proportion of preset tracks retained based on heading information near the highest box center; Determine the lower boundary and the upper boundary of the filtering heading according to the heading corresponding to the highest box center and the preset track retention ratio; The set of average headings of all the predicted tracks is filtered using the filtered heading lower boundary and the filtered heading upper boundary to obtain the filtered predicted track set.

2. The method for high-speed target motion track re-estimation based on compact high-frequency radar according to claim 1, characterized in that: The original radar echo data is subjected to a long-term coherent integration algorithm for a preset time period to obtain a target radial distance value and a target speed value, and a target arrival angle value is determined by a multiple signal classification algorithm, including: Determine the total number of radar sweep cycles, obtain the radial distance value and velocity value of any sweep cycle in the total number of radar sweep cycles through a long-term coherent integration algorithm in a preset time period, and obtain the arrival angle value of any sweep cycle in the total number of radar sweep cycles through a multiple signal classification algorithm.

3. The method for high-speed target motion track re-estimation based on compact high-frequency radar according to claim 1, characterized in that: The target original point trace is calculated according to the target radial distance value and the target arrival angle value, including: The target original point trace is obtained by multiplying the target radial distance value of any sweep frequency cycle by the sine value and cosine value of the corresponding arrival angle value.

4. The method for high-speed target motion track re-estimation based on compact high-frequency radar according to claim 1, characterized in that: Using the target speed value to perform track prediction on the target original point track to obtain a predicted track set, including: Determine the correlation coefficient between the radar angle of arrival measurement set and the radar sweep time series; Based on the target m The arrival angle value and radial distance value of the frequency sweep cycle, n The radial distance value of a sweep cycle, m Within the sweep cycle and n The distance difference of the target movement within the sweep frequency cycle and the correlation coefficient are used to determine the target n The target predicted arrival angle value of the sweep cycle is m =1,2,…, N , n =1,2,…, N , N is the number of sweep cycles; A predicted motion track is obtained by multiplying the target radial distance value of any sweep cycle with the sine and cosine values of the corresponding predicted arrival angle value; Repeat the above steps to obtain the predicted track set.

5. The method for high-speed target motion track re-estimation based on compact high-frequency radar according to claim 4, characterized in that: Determine the correlation coefficient between the radar angle of arrival measurement set and the radar sweep time series, including: Determining the radar arrival angle measurement set and the radar frequency sweep time series set; Calculating the variance of the arrival angle measurement set to obtain a first variance, calculating the variance of the radar frequency sweep time series set to obtain a second variance, and calculating the covariance of the arrival angle measurement set and the radar frequency sweep time series set; The correlation coefficient is obtained according to the covariance, the first variance, and the second variance.

6. The method for high-speed target motion track re-estimation based on compact high-frequency radar according to claim 4, characterized in that: Based on the target m The arrival angle value and radial distance value of the frequency sweep cycle, n The radial distance value of a sweep cycle, m Within the sweep cycle and n The distance difference of the target movement within the sweep frequency cycle and the correlation coefficient are used to determine the target n The target predicted arrival angle value for each sweep cycle includes: Determine the target m The radial distance value and arrival angle value of the frequency sweep cycle, the target n The radial distance value of a sweep cycle, m Within the sweep cycle and n The distance difference of the target movement within a sweep cycle; Based on the target m The radial distance value of the frequency sweep cycle, the target n The radial distance value of the sweep cycle, m Within the sweep cycle and n The distance difference of the target movement within a sweep cycle is used to obtain the arrival angle adjustment angle; If confirmed m and n Not equal, and m and n The product of the difference and the correlation coefficient is less than 0, then the target m The arrival angle value of the frequency sweep cycle is added to the arrival angle adjustment angle to obtain the target n The predicted arrival angle value of a sweep cycle; If confirmed m and n Not equal, and m and n The product of the difference and the correlation coefficient is greater than 0, then the target m The arrival angle value of the frequency sweep cycle minus the arrival angle adjustment angle is obtained to obtain the target n The predicted arrival angle value of a sweep cycle; If confirmed m equal n , then the target n The target predicted arrival angle value of the frequency sweep cycle is equal to the target m The arrival angle value of a sweep cycle.

7. The method for high-speed target motion track re-estimation based on compact high-frequency radar according to claim 1, characterized in that: The filtered predicted track set is integrated to obtain the final track result, including: Based on the total number of radar sweep cycles and the preset track retention ratio, the filtered predicted track set is averaged and fused to obtain the final track result.

8. A high-speed target motion track re-estimation system based on a compact high-frequency radar, characterized in that: include: a detection module, configured to receive raw radar echo data, perform a long-term coherent integration algorithm on the raw radar echo data for a preset time period to obtain a target radial distance value and a target velocity value, and determine a target arrival angle value through a multiple signal classification algorithm; a calculation module, configured to calculate a target original track according to the target radial distance value and the target arrival angle value, and perform track prediction on the target original track using the target speed value to obtain a predicted track set; a screening module, configured to calculate an average heading of each predicted track in the predicted track set, and screen the predicted track set according to a heading threshold to obtain a screened predicted track set; A fusion module is used to fuse the filtered predicted track set to obtain a final track result; The screening module is specifically used for: Obtain any predicted track in the predicted track set; Determining the longitude and latitude of any frequency sweep cycle of any predicted track, and the longitude and latitude of the next cycle of any frequency sweep cycle; Determine a first target position based on the longitude and latitude of any one frequency sweep cycle, and determine a second target position based on the longitude and latitude of a next cycle of any one frequency sweep cycle; Calculating the heading from the first target position to the second target position, summing and averaging all headings of any predicted track in the total number of radar sweep cycles to obtain an average heading of any predicted track; Repeat the above steps to obtain the set of average headings of all predicted tracks; Performing statistical analysis on the set of average headings of the predicted tracks based on a statistical histogram, obtaining the heading corresponding to the highest box center in the histogram, and determining the proportion of preset tracks retained based on heading information near the highest box center; Determine the lower boundary and the upper boundary of the filtering heading according to the heading corresponding to the highest box center and the preset track retention ratio; The set of average headings of all the predicted tracks is filtered using the filtered heading lower boundary and the filtered heading upper boundary to obtain the filtered predicted track set.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for re-estimating the high-speed target motion track based on a compact high-frequency radar is implemented as described in any one of claims 1 to 7.

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