Multi-target Track Real-time Fast Association Method, Device, Computer Equipment and Medium

Abstract

The present invention relates to the field of radar tracking technology, and discloses a method, device, computer device and medium for real-time fast association of multi-target tracks. The method includes obtaining a track list at the current moment and a set of scanning points of a radar; respectively pre-associating the set of scanning points with each track in the track list to obtain a set of pre-associated points corresponding to each track; calculating the optimal solution of the sum of distances between the pre-associated points in each pre-association set and the first predicted points of the next track points of their corresponding tracks based on the hill-climbing algorithm; and adding the pre-associated points corresponding to the optimal solution of the sum of distances to the ends of the corresponding tracks for association. The present invention solves the problems of large difficulty and low accuracy in target track association when existing MIMO radars track multi-targets.

Description

Technical Field

[0001] The present invention relates to the technical field of radar tracking, and specifically refers to a method, device, computer equipment and medium for real-time and fast association of multi-target tracks. Background Art

[0002] Multiple Input Multiple Output (MIMO) radar is a new type of radar that introduces the multiple input and multiple output technologies in wireless communication systems into the radar field and combines them with digital array technology. Due to the adoption of waveform diversity technology, MIMO radar has many advantages that traditional phased array radars cannot match.

[0003] The general method for selecting multi-target tracks of existing MIMO radars is to calculate the target position range based on speed and acceleration, and then add limiting conditions according to the results to select associated target points. However, with the popularization of the application of unmanned aerial vehicles, the number of main monitoring targets of MIMO radar has increased significantly, the target acceleration range has increased, and the target speed has changed from mainly medium and high speeds to mainly medium and low speeds. The difficulty of accurately associating track points has increased. Summary of the Invention

[0004] Based on the above technical problems, the present invention provides a method, device, computer equipment and medium for real-time and fast association of multi-target tracks, which solves the problems of large difficulty and low accuracy in target track association when existing MIMO radars track multi-targets.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for real-time and fast association of multi-target tracks includes:

[0007] Obtain the track list at the current moment and the set of scanning points of the radar;

[0008] Pre-associate the set of scanning points with each track in the track list respectively to obtain a set of pre-associated points corresponding to each track;

[0009] Calculate the optimal solution of the sum of distances between the pre-associated points in each pre-association set and the first predicted points of the next track points of their corresponding tracks based on the hill climbing algorithm;

[0010] Add the pre-associated points corresponding to the optimal solution of the sum of distances to the end of the corresponding track for association;

[0011] Among them, the pre-association includes:

[0012] Select target points based on the set of scanning points;

[0013] Select target tracks based on the track list;

[0014] Obtain the filtered point of the target point and the first predicted point of the next track point of the target track based on the Kalman algorithm;

[0015] Calculate the distance between the end point of the target track and the filtered point to obtain the first distance;

[0016] Calculate the distance between the first predicted point and the filtered point to obtain the second distance;

[0017] Construct an ellipsoid with the end point and the predicted point as the foci and the sum of the first distance and half of the second distance as the major semi-axis;

[0018] If the filtered point is within the range of the ellipsoid, then use the filtered point as the pre-associated point and put it into the pre-associated point set corresponding to the target track.

[0019] Furthermore, if the number of pre-associated points in the pre-associated point set corresponding to the track is zero or no successful association is made with any pre-associated point, then the track association fails.

[0020] Furthermore, if the number of track association failure times is greater than three, then the target corresponding to the track is lost.

[0021] Furthermore, if the target point is not in the pre-associated point set of any track or the pre-associated point is not the optimal solution of the distance sum, then use the target point or the pre-associated point as the re-associated point to perform re-association with the unassociated points that failed to be successfully associated at the previous cycle time;

[0022] Among them, the unassociated points that failed to be successfully associated at the previous cycle time are stored in the unassociated point set.

[0023] Furthermore, the re-association includes:

[0024] Obtain the second predicted point of each unassociated point in the unassociated point set based on the Kalman algorithm;

[0025] Calculate the optimal solution with the shortest distance between the re-associated point and each second predicted point based on the hill climbing algorithm;

[0026] Associate the re-associated point corresponding to the optimal solution with the shortest distance with the unassociated point corresponding to the second predicted point to form a new track and add it to the track list.

[0027] Furthermore, if the re-association of the re-associated point fails, then add the re-associated point as an unassociated point to the unassociated point set.

[0028] Furthermore, if the unassociated points still do not form a new track after two re-associations, then remove the unassociated points from the unassociated point set.

[0029] A multi-target track real-time fast association device, comprising:

[0030] A data acquisition module, which is used to acquire a track list and a set of scanning points of a radar;

[0031] A pre-association module, which is used to pre-associate the set of scanning points with each track in the track list respectively to obtain a set of pre-associated points corresponding to each track;

[0032] An association calculation module, the track association module is used to calculate the optimal solution of the sum of the distances between the pre-associated points in each pre-associated set and the first predicted points of the next track points of their corresponding tracks based on the hill climbing algorithm;

[0033] A track association module, which is used to add the pre-associated points corresponding to the optimal solution of the sum of the distances to the end of the corresponding track for association;

[0034] Among them, the pre-association includes:

[0035] Select target points based on the set of scanning points;

[0036] Select target tracks based on the track list;

[0037] Obtain the filtered points of the target points and the first predicted points of the next track points of the target tracks based on the Kalman algorithm;

[0038] Calculate the distance between the end point of the target track and the filtered point to obtain the first distance;

[0039] Calculate the distance between the first predicted point and the filtered point to obtain the second distance;

[0040] Construct an ellipsoid with the end point and the predicted point as the foci and the sum of the first distance and half of the second distance as the major semi-axis;

[0041] If the filtered point is within the range of the ellipsoid, the filtered point is used as a pre-associated point and put into the set of pre-associated points corresponding to the target track.

[0042] A computer device, including a memory and a processor, the memory stores a computer program, when the computer program is executed by the processor, the processor executes the steps of the above multi-target track real-time fast association method.

[0043] A computer-readable storage medium, storing a computer program, when the computer program is executed by the processor, the processor executes the steps of the above multi-target track real-time fast association.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] The present invention provides a method for processing target radar scan traces into target tracks in real time, which can quickly and relatively accurately process target tracks when the information on target speed and acceleration is not fully grasped. Specifically, it is reflected in the following aspects:

[0046] 1. Update the target track in real time;

[0047] 2. Small associated calculation amount;

[0048] 3. High correct association rate for medium and low speed targets. Description of the Drawings

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. Among them:

[0050] Figure 1 It is a schematic flow diagram of a multi-target track real-time fast association method.

[0051] Figure 2 It is a schematic pre-association flow diagram.

[0052] Figure 3 It is a schematic re-association flow diagram.

[0053] Figure 4 It is a structural block diagram of a multi-target track real-time fast association device.

[0054] Among them, 1 is a data acquisition module, 2 is a pre-association module, 3 is an associated calculation module, and 4 is a track association module. Specific Embodiments

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the drawings of the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of this application without creative efforts shall fall within the scope of protection of this application.

[0056] Refer to Figure 1 , in some embodiments, a multi-target track real-time fast association method includes:

[0057] S101. Obtain the track list at the current moment and the set of radar scan points;

[0058] Specifically, the radar scan points generally include data such as the longitude, latitude, altitude, speed, and acquisition time of the target points.

[0059] S102. Pre-associate the set of scan points with each track in the track list respectively to obtain the set of pre-associated points corresponding to each track.

[0060] S103. Calculate the optimal solution of the sum of distances between the pre-associated points in each pre-association set and the first predicted points of the next track points of their corresponding tracks based on the hill-climbing algorithm.

[0061] S104. Add the pre-associated points corresponding to the optimal solution of the sum of distances to the ends of the corresponding tracks for association.

[0062] Refer to Figure 2 , where pre-association includes:

[0063] S201. Select a target point based on the set of scan points.

[0064] Among them, the target point is a scan point selected from the set of scan points. Pre-association is to initially associate by selecting a scan point as the target point with the target track.

[0065] S202. Select a target track based on the track list.

[0066] Among them, the target track is a track selected from the track list.

[0067] S203. Obtain the filtered point of the target point and the first predicted point of the next track point of the target track based on the Kalman algorithm.

[0068] Among them, the filtered point refers to the target point corrected by Kalman filtering, which is generally performed in three dimensions of longitude, latitude, and altitude, while the predicted point refers to the next track point predicted after the end point of the target track.

[0069] Specifically, according to the gain generated during the filtering process of the target track by the Kalman algorithm, generate the predicted point of the next track point of the target track.

[0070] S204. Calculate the distance between the end point of the target track and the filtered point to obtain the first distance.

[0071] S205. Calculate the distance between the first predicted point and the filtered point to obtain the second distance.

[0072] S206. Construct an ellipsoid with the end point and the predicted point as the foci and the sum of the first distance and half of the second distance as the major semi-axis; specifically, let the first distance be L 1 and the second distance be L 2 and the major semi-axis be a, then the major semi-axis is specifically:

[0073]

[0074] S207, if the filtering point is within the ellipsoid range, then the filtering point is used as a pre-associated point and put into the set of pre-associated points corresponding to the target track.

[0075] Preferably, if the number of pre-associated points in the set of pre-associated points corresponding to the track is zero or no successful association is made with any pre-associated point, the track association fails.

[0076] Among them, the failure of track association means that this track fails to successfully associate with any target scan point obtained by the radar at the current moment.

[0077] Preferably, if the number of track association failures is greater than three, the target corresponding to the track is lost.

[0078] In some embodiments, if the target point is not in the set of pre-associated points of any track or the pre-associated point is not the optimal solution of the sum of distances, then the target point or the pre-associated point is used as a re-associated point to re-associate with the unassociated points that failed to be successfully associated at the previous cycle moment;

[0079] Among them, the unassociated points that failed to be successfully associated at the previous cycle moment are stored in the set of unassociated points.

[0080] Among them, the target point not being in the set of pre-associated points of any track or the pre-associated point not being the optimal solution of the sum of distances indicates that the scan point of this target fails to be successfully associated with any track, and this situation may indicate the appearance of a new flying object.

[0081] Refer to Figure 3 , preferably, the re-association includes:

[0082] S301, based on the Kalman algorithm, obtain the second predicted points of the next track points of each unassociated point in the set of unassociated points;

[0083] S302, based on the hill-climbing algorithm, calculate the optimal solution with the shortest distance between the re-associated point and each second predicted point;

[0084] Among them, the shortest distance means selecting the re-associated point with the shortest distance to the second predicted point of the unassociated point to associate with the unassociated point.

[0085] S303, associate the re-associated point corresponding to the optimal solution with the shortest distance with the unassociated point corresponding to the second predicted point to form a new track and add it to the track list.

[0086] Among them, the purpose of re-association is to determine a new track to track the target newly scanned by the radar.

[0087] Preferably, if the re-association of the re-associated point fails, the re-associated point is added to the set of unassociated points as an unassociated point.

[0088] Preferably, if the unassociated point still does not form a new track after two re-associations, the unassociated point is removed from the unassociated point set.

[0089] A multi-target track real-time fast association device, comprising:

[0090] A data acquisition module 1, which is used to acquire a track list and a set of radar scanning points;

[0091] A pre-association module 2, which is used to pre-associate the set of scanning points with each track in the track list respectively to obtain a set of pre-associated points corresponding to each track;

[0092] An association calculation module 3, and a track association module 4 is used to calculate the optimal solution of the sum of the distances between the pre-associated points in each pre-association set and the first predicted points of the next track points of their corresponding tracks based on the hill-climbing algorithm;

[0093] A track association module 4, which is used to add the pre-associated points corresponding to the optimal solution of the sum of the distances to the end of the corresponding track for association;

[0094] Among them, the pre-association includes:

[0095] Select target points based on the set of scanning points;

[0096] Select target tracks based on the track list;

[0097] Obtain the filtered points of the target points and the first predicted points of the next track points of the target tracks based on the Kalman algorithm;

[0098] Calculate the distance between the end point of the target track and the filtered point to obtain the first distance;

[0099] Calculate the distance between the first predicted point and the filtered point to obtain the second distance;

[0100] Construct an ellipsoid with the end point and the predicted point as the foci and the sum of the first distance and half of the second distance as the major semi-axis;

[0101] If the filtered point is within the range of the ellipsoid, the filtered point is used as a pre-associated point and put into the set of pre-associated points corresponding to the target track.

[0102] In some embodiments, the present application also discloses a computer device, including a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor is caused to execute the steps of the above multi-target track real-time fast association method.

[0103] Among them, the computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can interact with the user through a keyboard, a mouse, a remote control, a touchpad, a voice control device, or other means.

[0104] The memory at least includes one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or D interface display memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Of course, the memory may also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the memory is commonly used to store the operating system and various application software installed on the computer device, such as the program code of the multi-target track real-time fast association method. In addition, the memory can also be used to temporarily store various data that have been output or will be output.

[0105] In some embodiments, the processor can be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data, such as running the program code of the multi-target track real-time fast association method.

[0106] In some embodiments, the present application also discloses a computer-readable storage medium. A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor is caused to execute the steps of the above multi-target track real-time fast association method.

[0107] Among them, the computer-readable storage medium stores an interface display program, and the interface display program can be executed by at least one processor so that the at least one processor executes the steps of the program code of the multi-target track real-time fast association method as described above.

[0108] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0109] The above is the embodiment of the present invention. The above embodiments and the specific parameters in the embodiments are only for clearly expressing the verification process of the invention and are not used to limit the patent protection scope of the present invention. The patent protection scope of the present invention still takes its claims as the criterion. Any equivalent structural changes made by using the description and drawings of the present invention should, by the same token, be included in the protection scope of the present invention.

Claims

1. A real-time fast multi-target track association method, characterized in that, it includes: Obtain the track list at the current moment and the set of radar scanning points; Pre-associate the set of scanning points with each track in the track list respectively to obtain a set of pre-associated points corresponding to each track; Based on the hill-climbing algorithm, calculate the optimal solution of the sum of the distances between the pre-associated points in each pre-associated set and the first predicted points of the next track points of their corresponding tracks; Add the pre-associated points corresponding to the optimal solution of the sum of the distances to the end of the corresponding track for association; The pre-association includes: Select target points based on the set of scanning points; Select target tracks based on the track list; Based on the Kalman algorithm, obtain the filtered points of the target points and the first predicted points of the next track points of the target tracks; Calculate the distance between the end point of the target track and the filtered point to obtain the first distance; Calculate the distance between the first predicted point and the filtered point to obtain the second distance; Construct an ellipsoid with the end point and the predicted point as the foci and the sum of the first distance and half of the second distance as the major semi-axis; If the filtered point is within the range of the ellipsoid, then use the filtered point as a pre-associated point and put it into the set of pre-associated points corresponding to the target track.

2. The real-time fast multi-target track association method according to claim 1, characterized in that: If the number of pre-associated points in the set of pre-associated points corresponding to the track is zero or the association with any pre-associated point fails, the track association fails.

3. The real-time fast multi-target track association method according to claim 2, characterized in that: If the number of track association failure times is greater than three, the target corresponding to the track is lost.

4. The real-time fast multi-target track association method according to claim 1, characterized in that: If the target point is not in the set of pre-associated points of any track or the pre-associated point is not the optimal solution of the sum of the distances, then use the target point or the pre-associated point as a re-association point to re-associate with the unassociated points that failed to be successfully associated at the previous cycle time; Among them, the unassociated points that failed to be successfully associated at the previous cycle time are stored in the unassociated point set.

5. The real-time fast multi-target track association method according to claim 4, characterized in that, the re-association includes: Based on the Kalman algorithm, obtain the second predicted points of the next track points of each unassociated point in the unassociated point set; Based on the hill-climbing algorithm, calculate the optimal solution with the shortest distance between the re-association point and the second predicted point; Associate the re-association point corresponding to the optimal solution with the shortest distance with the unassociated point corresponding to the second predicted point to form a new track and add it to the track list.

6. The real-time fast multi-target track association method according to claim 5, characterized in that: If the re-association of the re-association point fails, then add the re-association point as an unassociated point to the unassociated point set.

7. The real-time fast multi-target track association method according to claim 4, characterized in that: If the unassociated points still do not form a new track after two re-associations, then remove the unassociated points from the unassociated point set.

8. A real-time fast multi-target track association device, characterized in that, it includes: A data acquisition module, which is used to acquire a track list and a set of scanning points of a radar; A pre-association module, which is used to pre-associate the set of scanning points with each track in the track list respectively to obtain a set of pre-associated points corresponding to each track; An association calculation module, the track association module is used to calculate the optimal solution of the sum of the distances between the pre-associated points in each pre-association set and the first predicted points of the next track points of their corresponding tracks based on the hill climbing algorithm; A track association module, the track association module is used to add the pre-associated points corresponding to the optimal solution of the sum of the distances to the end of the corresponding track for association; Wherein, the pre-association includes: Selecting a target point based on the set of scanning points; Selecting a target track based on the track list; Obtaining the filtered point of the target point and the first predicted point of the next track point of the target track based on the Kalman algorithm; Calculating the distance between the end point of the target track and the filtered point to obtain a first distance; Calculating the distance between the first predicted point and the filtered point to obtain a second distance; Constructing an ellipsoid with the end point and the predicted point as the foci and the sum of the first distance and half of the second distance as the major semi-axis; If the filtered point is within the range of the ellipsoid, the filtered point is used as a pre-associated point and put into the set of pre-associated points corresponding to the target track.

9. A computer device, characterized in that, it includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the multi-target track real-time fast association method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, it stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the multi-target track real-time fast association method according to any one of claims 1 to 7.

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