A compact ground wave radar point track-track correlation method and system

Abstract

The present application discloses a compact ground wave radar point track-track association method and system, which belongs to the technical field of over-the-horizon surveillance and monitoring of marine ship targets; it includes obtaining R-D spectrum data, obtaining an initial point track set according to the R-D spectrum data; obtaining the quality index of each point track according to the R-D spectrum data and the initial point track set; eliminating abnormal point tracks according to the detection distance of the compact ground wave radar, the moving speed of the target ship and the quality index of each point track; based on the point track set after eliminating abnormal point tracks, obtaining associated point tracks according to the target track, point track quality index and kinematic parameters; updating the target track according to the associated point tracks. A new point track quality index is proposed, which updates the target track by comprehensively selecting candidate point tracks in the associated wave gate using the point track quality index and kinematic parameters in the point track-track association process, thereby solving the problem of "low accuracy of point track-track association due to interference from false point tracks" in the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of over-the-horizon surveillance and monitoring of marine vessel targets, and in particular to a compact ground wave radar point track-track correlation method and system. Background Art

[0002] The statements in this section merely mention background technology related to the present application and do not necessarily constitute prior art.

[0003] High Frequency Surface Wave Radar (HFSWR) is a means of over-the-horizon detection of moving targets at sea, with the advantages of large observation range, all-weather operation and low cost. Large array radar receiving antenna array aperture is large, which can accurately locate targets at sea, but its high cost, large footprint, deployment and maintenance difficulties limit its promotion and application. Therefore, compact surface wave radar with low cost, small footprint, convenient and flexible deployment has become a development trend of surface wave radar system. However, compact surface wave radar has low transmission power and large beam width, and is easily affected by clutter and interference during target detection, resulting in low signal to noise ratio (SNR) of target echo. In order to improve the probability of weak target detection, a low constant false alarm rate (CFAR) detection threshold is usually set during target detection.

[0004] However, complex detection background and low detection threshold will lead to a large number of false points, which will affect the performance of subsequent track tracking. This is mainly manifested in the following aspects: (1) False points are mixed with real target points, which may easily lead to mistracking and track breakage due to point-track association errors; (2) In an environment with high clutter density, a large number of false tracks are easily generated, making it impossible for the radar to make timely judgments on potential threats; (3) Too many points will lead to saturation of the data processing system, affecting the processing speed and detection performance of the entire radar system. Summary of the invention

[0005] The inventors note that the CFAR detection algorithm uses the echo amplitude to determine whether the radar echo signal contains a target, but in a complex background, it is difficult to effectively distinguish the target, clutter and noise using only the amplitude, and a large number of false alarms are easily generated. In fact, in addition to the amplitude, the spatial structure, position distribution and other characteristics of the point trace in the range-Doppler (Range-Doppler, RD) spectrum can also be obtained, and such characteristics can effectively distinguish the target, clutter and noise. In order to determine the possibility that the point trace is a real target or a false point trace, the point trace quality can be evaluated according to the differences in the spatial structure and position distribution of the target, clutter and noise on the RD spectrum, and the obtained point trace quality index is used to assist in improving the accuracy of subsequent point trace-track association.

[0006] In order to address the deficiencies of the prior art, the present application provides a compact ground wave radar point track-track association method, which can increase the tracking time of the compact ground wave radar on the target and improve the tracking continuity of the compact ground wave radar in a clutter-dense environment.

[0007] In a first aspect, the present application provides a compact ground wave radar point track-track correlation method;

[0008] A compact ground wave radar point track-track correlation method, comprising:

[0009] Obtain RD spectrum data, and obtain an initial point trace set according to the RD spectrum data;

[0010] According to the RD spectrum data and the initial point trace set, the quality index of each point trace is obtained;

[0011] According to the detection distance of the compact ground wave radar, the moving speed of the target ship and the quality index of each point trace, the abnormal points are eliminated;

[0012] Based on the point set after eliminating abnormal points, the associated points are obtained according to the target track, point quality index and kinematic parameters;

[0013] Update the target track according to the associated point track.

[0014] In a second aspect, the present application provides a compact ground wave radar point track-track correlation system;

[0015] A compact ground wave radar point track-track correlation system, comprising:

[0016] An initial point trace acquisition module is used to acquire RD spectrum data, and acquire an initial point trace set according to the RD spectrum data;

[0017] A point trace quality index acquisition module is used to obtain the quality index of each point trace according to the RD spectrum data and the initial point trace set;

[0018] The abnormal point trace removal module removes abnormal points according to the detection distance of the compact ground wave radar, the moving speed of the target ship and the quality index of each point trace;

[0019] The associated point track acquisition module is used to obtain the associated point tracks based on the point track set after the abnormal point tracks are eliminated, according to the target track, the point track quality index and the kinematic parameters;

[0020] The target track update module is used to update the target track according to the associated point track.

[0021] In a third aspect, the present application provides an electronic device;

[0022] An electronic device comprises a memory and a processor and computer instructions stored in the memory and running on the processor. When the computer instructions are run by the processor, the steps of the above-mentioned compact ground wave radar point track-track association method are completed.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium;

[0024] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned compact ground wave radar point track-track correlation method are completed.

[0025] Compared with the prior art, the beneficial effects of this application are:

[0026] 1. This application utilizes the differences in spatial structure, position distribution, etc. between the target and clutter and noise on the RD spectrum, and proposes a point track quality index that integrates multi-dimensional features of multi-directional gradient values, local variance, point track position, and point track beam number. When the point track is associated with the track, the point track quality index and kinematic parameters are combined to comprehensively judge the candidate point tracks in the associated wave gate. According to the difference in the point track quality index between the target and clutter and noise, some clutter and noise point tracks can be filtered out, thereby reducing the calculation burden of the tracking algorithm and weakening the influence of false alarms on the accuracy of point track-track association.

[0027] 2. By using the point quality index, the probability of associating with the ship target point can be increased during the tracking process, and the accuracy of the association can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding 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 improper limitations on the present application.

[0029] Figure 1 A schematic diagram of a process flow provided for an embodiment of the present application;

[0030] Figure 2 A schematic diagram of multi-directional gradients for extracting multi-directional gradient value features of point traces provided in an embodiment of the present application;

[0031] Figure 3 A schematic diagram of selecting a local variance reference window for extracting local variance features of point traces provided in an embodiment of the present application;

[0032] Figure 4 A schematic diagram of RD spectrum area division for position feature extraction of point traces provided in an embodiment of the present application;

[0033] Figure 5 A schematic diagram of a compact ground wave radar point track-track association provided in an embodiment of the present application;

[0034] Figure 6 This is a graph showing the quality assessment results of a compact ground wave radar RD spectrum point trace using an embodiment of the present application;

[0035] Figure 7 The dot plots of the 5 real targets selected for the application of the embodiment of the present application on the RD spectrum are shown below: Figure 7 (a) is a dot plot of five real targets detected by a compact ground wave radar. Figure 7 (b) is a display of the AIS traces corresponding to the five real targets;

[0036] Figure 8 A distribution diagram of false points detected by a compact ground wave radar selected for application of the embodiment of the present application in a geographic coordinate system;

[0037] Fig. 9 A curve diagram showing a change in target point quality index of a compact ground wave radar using an embodiment of the present application;

[0038] Fig.10 A point trace quality index distribution diagram of false point traces detected by a certain compact ground wave radar using an embodiment of the present application;

[0039] Fig.11 A comparison chart of average point trace quality indicators of real targets and false point traces detected by a compact ground wave radar according to an embodiment of the present application;

[0040] Fig.12 A comparison diagram of tracking results of a compact ground wave radar track example 1 using an embodiment of the present application;

[0041] Fig.13 This is a comparison chart of tracking results of Example 2 of a compact ground wave radar track using an embodiment of the present application. DETAILED DESCRIPTION

[0042] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present application have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0045] Embodiment 1

[0046] In the prior art, classifying the points obtained by target detection, further distinguishing target points from clutter points, and reducing the interference of false points on the data association process is a key problem that needs to be solved urgently in compact ground wave radar target detection; therefore, the present application provides a compact ground wave radar point track-track association method.

[0047] A compact ground wave radar point track-track correlation method, comprising:

[0048] Obtain RD spectrum data, and obtain an initial point trace set according to the RD spectrum data;

[0049] According to the RD spectrum data and the initial point trace set, the quality index of each point trace is obtained;

[0050] According to the detection distance of the compact ground wave radar, the moving speed of the target ship and the quality index of each point trace, the abnormal points are eliminated;

[0051] Based on the point set after eliminating abnormal points, the associated points are obtained according to the target track, point quality index and kinematic parameters;

[0052] Update the target track according to the associated point track.

[0053] Furthermore, the step of obtaining the quality index of each point trace according to the RD spectrum data and the initial point trace set includes:

[0054] According to the RD spectrum data and the initial point trace set, characteristic parameters of each point trace are obtained; wherein the characteristic parameters include multi-directional gradient values, local variance, point trace position and point trace beam number;

[0055] According to the characteristic parameters of each point trace, the quality index of each point trace is obtained.

[0056] Furthermore, the method further includes: dividing the point trace set after removing abnormal points into one to four levels according to the point trace quality index.

[0057] Furthermore, the point trace quality index is

[0058] Q = a G S G +a V S V +a L S L +a B S B

[0059] Among them, Q is the point quality index, a G 、a V 、a L 、a B is the weight, a G +a V +a L +a B =1, S G is the eigenvalue after normalization of multi-directional gradient values, S V is the eigenvalue after local variance normalization, S L is the eigenvalue after normalization of the point position, S B is the eigenvalue after normalization of the point trace beam number.

[0060] Furthermore, based on the point track set after eliminating abnormal point tracks, obtaining the associated point tracks according to the target track, the point track quality index and the kinematic parameters includes:

[0061] According to the kinematic parameters and the target track, the track prediction state is obtained, and according to the track prediction state and the point set after the abnormal points are removed, the similarity between each point and the aerial survey prediction state is obtained;

[0062] According to the similarity, the association cost set and the minimum association cost of each point track and the target track are obtained;

[0063] Based on the association cost set and the minimum association cost, the association traces are obtained according to the trace quality index.

[0064] Next, combine Figure 1-13 A compact ground wave radar point track-track correlation method disclosed in this embodiment is described in detail.

[0065] This embodiment provides a compact ground wave radar point track-track association method.

[0066] A compact ground wave radar point track-track correlation method, comprising:

[0067] S1. Obtain RD spectrum data, and obtain an initial point trace set based on the RD spectrum data; specifically, use a CFAR detection algorithm (such as CA-CFAR, OS-CFAR, etc.) and a direction finding algorithm (such as amplitude comparison direction finding method, DBF algorithm, MUSIC algorithm, etc.) to perform target detection on the RD spectrum to obtain an initial point trace set.

[0068] S2. Obtaining the quality index of each point trace according to the RD spectrum data and the initial point trace set; including:

[0069] S201, according to the RD spectrum data and the initial point trace set, obtain the characteristic parameters of each point trace; wherein the characteristic parameters include multi-directional gradient values, local variance, point trace position and point trace beam number; the steps of obtaining the characteristic parameters are as follows:

[0070] S2011, such as Figure 2 As shown in the figure, eight cells around the point are selected as multi-directional gradient templates. Formula (1) is used to calculate the gradient values ​​of the peak point and the eight surrounding directions respectively:

[0071]

[0072] Among them, Grad i is the gradient value in each direction, A i are the amplitude values ​​corresponding to the surrounding 8 cells, and A0 is the amplitude value of the cell where the peak point is located.

[0073] In addition to the target point, there are inevitably clutter points or noise points in the RD spectrum that have large fluctuations and meet the characteristics of multi-directional gradient descent. However, the target point usually appears as a strong isolated peak with a large multi-directional gradient descent value. Therefore, it is necessary to set the gradient threshold U and the direction number threshold V. If Grad i If the number of gradient values ​​exceeding the threshold U is greater than V, it is considered that the point is more likely to be derived from the real target. The more gradient values ​​exceeding the threshold U, the greater the possibility that the point is the real target.

[0074] Sea clutter, ground clutter, and ionospheric clutter are mostly distributed continuously over a large area in the RD spectrum, and the energy intensity is gradually changing along the distance or velocity direction; target points are usually isolated points or clusters, and the energy intensity has a characteristic of rapid gradient decline in the eight directions around the peak point; noise points also appear in the form of points or clusters, but the energy intensity is low and the multi-directional gradient decline characteristics are not obvious. By analyzing the changes in multi-directional gradients, real targets and false points can be preliminarily distinguished.

[0075] S2012, such as Figure 3 As shown in the figure, according to the widening of sea clutter and ground clutter and the diffusion range of the target, a suitable rectangular window is selected to represent the local neighborhood of the unit to be detected. It can be seen from the enlarged figure that the extended unit cell size of the target on the RD spectrum is about 3×3, and the local variance reference window needs to be larger than the target diffusion range. The local variance reference window size selected in this disclosure is 3×5 (such as W1, W2, W3). The local variance of the point trace is calculated by formula (2):

[0076]

[0077] Where N is the number of cells contained in the reference window, D(j) is the amplitude value in the jth cell, and μ is the mean amplitude value in the reference window.

[0078] Figure 3 In the figure, W1, W2, and W3 are the local variance reference windows of the target, ground clutter area, and background noise area, respectively. It can be seen that the amplitude dispersion of the target neighborhood is larger than that of the clutter area and background noise area. Therefore, the local variance of the real target is usually greater than that of the false point trace.

[0079] Although the multi-directional gradient value can be used to preliminarily identify the point traces, when the target is at the edge of the clutter, the gradient value close to the clutter side is usually lower than the threshold U. Therefore, new features are needed to distinguish this type of point traces.

[0080] The local variance can reflect the amplitude discreteness in the local neighborhood of the unit to be detected. The spatial correlation of ground clutter, sea clutter, and ionospheric clutter is strong, the energy distribution is uniform, and the local variance is small; the amplitude difference between the target point and the background is large, the spatial correlation is weak, and the local variance is large; the false point traces formed by noise are not much different from the background amplitude, and the local variance is small. And the local variance of the point traces in the clutter area is usually smaller than the local variance of the point traces at the edge of the clutter.

[0081] S2013, such as Figure 4As shown in the figure, the RD spectrum is divided into regions, T1, S1, G1, and B1 are target area, sea clutter area, ground clutter area, and background noise area respectively. The probability of points in the region being real targets is usually ranked as T1>S1>G1>B1. By statistically analyzing the position of points in a large amount of measured data, the percentage of real targets distributed in different regions is determined.

[0082] Since ionospheric clutter is usually distributed within a range of 200 km from the radar, which exceeds the maximum detection distance of compact ground wave radar for targets, its impact on target detection can be ignored.

[0083] Since the clutter energy is not completely uniformly distributed, there may be some false points located in the clutter area, whose multi-directional gradient values ​​and local variances are similar to those of the real targets. The target points and clutter points can be further distinguished according to the point position.

[0084] S2014. Determine the point track beam number of the point track according to the number of beams that detect a point track. The antenna beam of the compact ground wave radar is relatively wide, and the same target may be detected by multiple beams at the same time. The point track beam number indicates the number of beams that detect a point track at the same time. The possibility that the point track originates from a real target can be judged according to the point track beam number. It is generally believed that the point track with more point track beam numbers has a greater possibility of being a real target.

[0085] S202, obtaining the quality index of each point trace according to the characteristic parameters of each point trace; the specific process includes:

[0086] S2021. Since the numerical values ​​and magnitudes of the above four indicators vary greatly, normalization processing is required to eliminate the influence of unit and scale differences between different features. In this embodiment, (0, 1) normalization is adopted for normalization processing, and each eigenvalue X is normalized to [0, 1] by formula (3):

[0087]

[0088] Among them, X nor is the normalized eigenvalue, X min , X max Respectively represent the minimum and maximum values ​​of the data before normalization.

[0089] S2022. Since different point trace features have different importance for point trace quality assessment, the normalized point trace feature values ​​are weighted and fused to comprehensively assess the point trace quality. G , S V , S L , S BThey represent the normalized eigenvalues ​​of multi-directional gradient values, local variance, point position, and point beam number, respectively. Orthogonal experiments are designed to analyze the point quality evaluation effects under different weight combinations and determine the optimal weight combination (a G 、a V 、a L 、a B ), the weight setting must satisfy the condition of formula (4):

[0090] a G +a V +a L +a B =1 (4)

[0091] The final point trace quality index Q can be calculated by formula (5):

[0092] Q = a G S G +a V S V +a L S L +a B S B (5)

[0093] The larger the point quality index is, the more likely it is that the point is derived from the real target. Otherwise, it may be derived from clutter or noise.

[0094] S3, eliminating abnormal points in the initial point set; including:

[0095] S301, delete the points whose distance and speed values ​​do not satisfy equation (6).

[0096]

[0097] As an implementation method, in order to reduce the processing load of the tracker, the following steps may be performed: S302, setting a deletion threshold Q min , remove points with too small a quality index, and only keep points with a quality index greater than Q min The point trace of min The value of can be set by statistically analyzing the quality index range of the real target and the false points in the measured data.

[0098] As an implementation method, in order to facilitate the analysis of the differences between traces with different quality indicators, the following steps can be performed: S303, according to the calculated trace quality indicator, the trace quality is divided into different quantization levels. Specifically, the embodiment of the present application uses the K-Means clustering algorithm to divide the traces after the abnormal traces are removed into levels one to four according to the size of the trace quality indicator Q.

[0099] S4. Based on the point track set after eliminating abnormal points, according to the target track, point track quality index and kinematic parameters, obtain the associated point track; including:

[0100] S401, obtaining a track prediction state according to kinematic parameters and the target track, and obtaining a similarity between each point track and the aerial survey prediction state according to the track prediction state and a point track set after removing abnormal points;

[0101] S402, obtaining the association cost set and the minimum association cost of each point track and the target track according to the similarity;

[0102] S403 , based on the association cost set and the minimum association cost, and according to the point quality index, obtain the associated point traces.

[0103] S5. Update the target track according to the associated point track.

[0104] For example, let track k ={x1,x2,…x N} is a track containing N points tracked by the compact ground wave radar at time k, and the correlation gate at time k+1 contains M measurement points The superscript m indicates the measurement status. in, Indicates the Doppler velocity of the target, Indicates the distance between the target and the radar. Represents the azimuth of the target relative to the radar. The estimated state of the target at time k is predicted using a certain motion model (such as uniform linear motion) to obtain the state of the target at time k+1, which is expressed as The superscript p indicates the predicted state.

[0105] For any measurement point that falls within the associated wave gate Formula (7) is used to calculate the track k The cost of association between:

[0106] c=1-(c v +c r +c θ ) (7)

[0107] Among them, c v ,c r With c θ Respectively represent the measurement points Track prediction status The similarity between them in terms of Doppler velocity, distance and azimuth is calculated by equations (8)-(10):

[0108]

[0109]

[0110]

[0111] Among them, σ v , σ r and σ θ Represent the standard deviation of the three kinematic parameters of Doppler velocity, range and azimuth, W v , W r and W θ It represents the association weight of the three parameters, which is set according to the resolution of the parameters and needs to satisfy the condition of formula (11):

[0112] W v +W r +W θ =1 (11)

[0113] It is easy to see that c v 、c r With c θ The larger the value of is, the higher the similarity between the parameters is, the smaller the association cost c is, and the higher the probability that the measurement point track originates from the target track. Through the above calculation, the association cost set {c1,…,c M} and the minimum associated cost c min =min{c1,…,c M}.

[0114] Track of a target track obtained by radar tracking at time k k Take for example, its predicted position, associated wave gate and candidate point traces The relationship between Figure 5 As shown, The points generated by clutter have corresponding point quality indicators of Q1, Q3, Q4, and Q5. Track k The measurement point trace at time k has a point trace quality index of Q2.

[0115] With track k The association costs of are similar, but Q2 is much greater than Q1. Therefore, in order to improve the association accuracy, the point track quality index can be integrated into the point track-track association process, and the point track quality index can be used to judge the possibility that the candidate point track in the association gate is the real target, thereby improving the association result of the NNDA algorithm.

[0116] Use formula (12) to calculate the association cost and minimum association cost c of all candidate points respectively minDifference:

[0117] diff h =c h -c min (h=1,…,M) (12)

[0118] Selecting the point with the highest quality index among the points with similar association costs to associate with the target track can effectively avoid the point-track erroneous association problem caused by false point interference in clutter-dense environments. Set a smaller association cost threshold ε, if diff h ≤ε, the hth candidate point is classified as the point to be associated, and finally the set of points to be associated {p1,…,p f}(f≥1), and choose {p1,…,p f The point with the highest quality index Q is used as the associated point to filter and update the target track.

[0119] Since the Nearest Neighbor Data Association (NNDA) method is one of the earliest proposed and most effective data association methods, it is prone to point-track association errors when the clutter density is high. Therefore, the point-track quality index is used to correct the point-track association results of the NNDA method, reduce the point-track association errors caused by false point-track interference, and improve the radar's tracking performance of the target.

[0120] Embodiment 2

[0121] This embodiment discloses a compact ground wave radar point track-track association system, including:

[0122] An initial point trace acquisition module is used to acquire RD spectrum data, and acquire an initial point trace set according to the RD spectrum data;

[0123] A point trace quality index acquisition module is used to obtain the quality index of each point trace according to the RD spectrum data and the initial point trace set;

[0124] The abnormal point trace removal module removes abnormal points according to the detection distance of the compact ground wave radar, the moving speed of the target ship and the quality index of each point trace;

[0125] The associated point track acquisition module is used to obtain the associated point tracks based on the point track set after the abnormal point tracks are eliminated, according to the target track, the point track quality index and the kinematic parameters;

[0126] The target track update module is used to update the target track according to the associated point track.

[0127] It should be noted that the above-mentioned initial point track acquisition module, point track quality index acquisition module, abnormal point track rejection module, associated point track acquisition module and target track update module correspond to the steps in Example 1, and the examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0128] Embodiment 3

[0129] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned compact ground wave radar point track-track association method are completed.

[0130] Embodiment 4

[0131] Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned compact ground wave radar point track-track association method are completed.

[0132] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0133] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0135] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0136] The above description is only the preferred 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 modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A compact ground wave radar point track-track correlation method, characterized in that: include: Obtain RD spectrum data, and obtain an initial point trace set according to the RD spectrum data; According to the RD spectrum data and the initial point trace set, the quality index of each point trace is obtained, including: according to the RD spectrum data and the initial point trace set, the characteristic parameters of each point trace are obtained; wherein the characteristic parameters include multi-directional gradient values, local variances, point trace positions and point trace beam numbers; According to the characteristic parameters of each point trace, the quality index of each point trace is obtained. The point trace quality index is Among them, Q is the point quality index, , , , For weight, , is the eigenvalue after normalization of multi-directional gradient values, is the eigenvalue after local variance normalization, is the eigenvalue after normalization of the point position, is the eigenvalue after normalization of the point trace beam number; According to the detection distance of the compact ground wave radar, the moving speed of the target ship and the quality index of each point trace, the abnormal points are eliminated; Based on the point set after removing abnormal points, the associated points are obtained according to the target track, point quality index and kinematic parameters, including: According to the kinematic parameters and the target track, the track prediction state is obtained, and according to the track prediction state and the point set after the abnormal points are removed, the similarity between each point and the aerial survey prediction state is obtained; According to the similarity, the association cost set and the minimum association cost of each point track and the target track are obtained; Based on the association cost set and the minimum association cost, and according to the point quality index, the association point traces are obtained; Update the target track according to the associated point track.

2. The compact ground wave radar point track-track correlation method as claimed in claim 1, characterized in that: Also includes: According to the point trace quality index, the point trace set after removing abnormal points is divided into one to four levels.

3. The compact ground wave radar point track-track correlation method as claimed in claim 1, characterized in that: include: Setting the deletion threshold , remove points with quality index less than The dot trace.

4. The compact ground wave radar point track-track correlation method as claimed in claim 1, characterized in that: Perform target detection on the RD spectrum data to obtain an initial point trace set.

5. A compact ground wave radar point track-track correlation system, characterized in that: include: An initial point trace acquisition module is used to acquire RD spectrum data, and acquire an initial point trace set according to the RD spectrum data; A point trace quality index acquisition module is used to acquire the quality index of each point trace according to the RD spectrum data and the initial point trace set, including: acquiring the characteristic parameters of each point trace according to the RD spectrum data and the initial point trace set; wherein the characteristic parameters include multi-directional gradient values, local variance, point trace position and point trace beam number; According to the characteristic parameters of each point trace, the quality index of each point trace is obtained. The point trace quality index is Among them, Q is the point quality index, , , , For weight, , is the eigenvalue after normalization of multi-directional gradient values, is the eigenvalue after local variance normalization, is the eigenvalue after normalization of the point position, is the eigenvalue after normalization of the point trace beam number; The abnormal point trace removal module removes abnormal points according to the detection distance of the compact ground wave radar, the moving speed of the target ship and the quality index of each point trace; The associated point track acquisition module is used to obtain associated point tracks based on the point track set after eliminating abnormal point tracks, according to the target track, point track quality indicators and kinematic parameters, including: According to the kinematic parameters and the target track, the track prediction state is obtained, and according to the track prediction state and the point set after the abnormal points are removed, the similarity between each point and the aerial survey prediction state is obtained; According to the similarity, the association cost set and the minimum association cost of each point track and the target track are obtained; Based on the association cost set and the minimum association cost, and according to the point quality index, the association point traces are obtained; The target track update module is used to update the target track according to the associated point track.

6. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps described in any one of claims 1 to 4 are completed.

7. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps described in any one of claims 1 to 4.

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