Networking radar multi-target adaptive correlation tracking method, device and system and medium

Abstract

The invention provides a networking radar multi-target adaptive correlation tracking method, device and system and a medium, and relates to the technical field of radar data processing. According to the method, the target prediction data at the next moment are acquired through the Kalman filter, and the final radar measurement data at the next moment are determined by using the combined action of the target prediction data at the next moment, the measurement data of the target at the next moment and the adaptive tracking gate. According to the method, data within the non-detection precision of the current target in networking radars and other interference data are eliminated, so that final radar measurement data at the next moment can serve as optimal data to participate in trajectory determination of the current target, and in addition, trajectory fusion is performed on a plurality of tracking trajectories of all radars through a convex combination data fusion algorithm, so that the trajectory determination accuracy is improved. And the generation quality and efficiency of the overall tracking trajectory are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar data processing, and particularly relates to a multi-target adaptive association tracking method, device, system and medium for a networked radar. Background Art

[0002] Currently, in the target tracking technology of a single radar, due to the fixed viewing angle of the single radar, the limitations of radar accuracy and its own power range, the tracking and detection performance of the single radar is greatly restricted. Compared with a single radar, a networked radar can improve the resource utilization rate of the radar and obtain more redundant radar echo information. Therefore, the advantages of a networked radar system are obvious.

[0003] The existing multi-target tracking methods for networked radars generally use information entropy weight and the nearest neighborhood data association method (Nearest Neighborhood, abbreviated as NN) to optimize the target tracks. Among them, in the optimization design, the tracking gate setting of the existing technology is set according to the probability that the target falls into the tracking gate conforming to a certain distribution. Due to the problems of low data correlation, poor filtering results and easy generation of incorrect associations in the existing tracking gate setting method. Therefore, the accuracy of the existing multi-target tracking methods for networked radars is relatively low. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a multi-target adaptive association tracking method, device, system and medium for a networked radar.

[0005] The technical problems to be solved by the present invention are realized through the following technical solutions:

[0006] In a first aspect, the present invention provides a multi-target adaptive association tracking method for a networked radar, including:

[0007] Calculate the motion state of the target at the current moment according to the final radar measurement data at the current moment and the final radar measurement data at the previous moment;

[0008] Input the motion state of the target at the current moment into a Kalman filter to obtain the target prediction data at the next moment;

[0009] Determine the tracking gate error according to the final radar measurement data at the previous moment and the radar own error information, and sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment;

[0010] Input the target prediction data at the next moment, the measurement data of the target at the next moment and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment;

[0011] Connect the radar final measurement data at the next moment of the current radar with the radar final measurement data at the previous moment to obtain multiple tracking trajectories;

[0012] Fuse the multiple tracking trajectories of all radars through the convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target.

[0013] Optionally, calculate the motion state of the target at the current moment according to the radar final measurement data at the current moment and the radar final measurement data at the previous moment, including:

[0014] Obtain the position information of the target at the current moment according to the radar final measurement data at the current moment;

[0015] Subtract the radar final measurement data at the current moment from the radar final measurement data at the previous moment, and divide by the time difference between the current moment and the previous moment to obtain the speed information of the target at the current moment;

[0016] Take the position information of the target at the current moment and the speed information of the target at the current moment as the motion state of the target at the current moment.

[0017] Optionally, the radar self-error information includes: range error, azimuth error, pitch error, and combined error;

[0018] Among them, the combined error is the combined information including range error, azimuth error, and pitch error.

[0019] Optionally, determine the tracking gate error according to the radar final measurement data at the previous moment and the radar self-error information, and sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment, including:

[0020] Sum the radar final measurement data at the previous moment with the range error, azimuth error, pitch error, and combined error respectively to obtain multiple intermediate measurement data;

[0021] Calculate the Euclidean distances between the intermediate measurement data and the radar final measurement data at the previous moment respectively to obtain multiple initial tracking gate errors;

[0022] Sort the multiple initial tracking gate errors by size, and obtain the tracking gate error according to the sorting result;

[0023] Sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment.

[0024] Optionally, sort the multiple initial tracking gate errors by size, and obtain the tracking gate error according to the sorting result, including:

[0025] Take the initial tracking gate error with the largest value in the sorting result as the tracking gate error.

[0026] Optionally, input the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment, including:

[0027] The radar Euclidean distance formula is expressed as:

[0028]

[0029] Where is the radar Euclidean distance, k represents the current moment, j represents the j-th radar, where j takes values of 1 ≤ j ≤ N, and N is the total number of radars, is the measurement data of the target at the next moment, is the target prediction data at the next moment, γ k+1 is the adaptive tracking gate at the next moment.

[0030] Optionally, before inputting the motion state of the target at the current moment into the Kalman filter to obtain the target prediction data at the next moment, a multi-target adaptive association tracking method for a networked radar further includes:

[0031] Obtain the initial track of the target according to the final radar measurement data of the previous two moments at the current moment;

[0032] Use the initial track to initialize the Kalman filter.

[0033] In a second aspect, the present invention provides a multi-target adaptive association tracking device for a networked radar, including: a calculation unit, an acquisition unit, and a fusion unit;

[0034] The calculation unit is used to calculate the motion state of the target at the current moment according to the final radar measurement data at the current moment and the final radar measurement data at the previous moment;

[0035] The acquisition unit is used to input the motion state of the target at the current moment into the Kalman filter to obtain the target prediction data at the next moment;

[0036] The calculation unit is further used to determine the tracking gate error according to the final radar measurement data at the previous moment and the radar self-error information, and sum the tracking gate error and the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment;

[0037] The calculation unit is further used to input the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment;

[0038] The fusion unit is configured to connect the radar final measurement data at the next moment of the current radar with the radar final measurement data at the previous moment to obtain multiple tracking trajectories;

[0039] The fusion unit is further configured to perform trajectory fusion on the multiple tracking trajectories of all radars through a convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target.

[0040] In a third aspect, the present invention provides a multi-target adaptive association tracking system for a networked radar, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the multi-target adaptive association tracking system for a networked radar runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the method in the first aspect as described above.

[0041] In a fourth aspect, the present invention provides a storage medium, on which a computer program is stored. When the computer program is run by a processor, it performs the steps of the method in the first aspect as described above.

[0042] The present invention provides a multi-target adaptive association tracking method, device, system, and medium for a networked radar. Among them, the multi-target adaptive association tracking method for a networked radar includes: calculating the motion state of the target at the current moment according to the radar final measurement data at the current moment and the radar final measurement data at the previous moment; inputting the motion state of the target at the current moment into a Kalman filter to obtain the target prediction data at the next moment; determining the tracking gate error according to the radar final measurement data at the previous moment and the radar own error information, and summing the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment; inputting the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the radar final measurement data at the next moment; connecting the radar final measurement data at the next moment of the current radar with the radar final measurement data at the previous moment to obtain multiple tracking trajectories; performing trajectory fusion on the multiple tracking trajectories of all radars through a convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target. In the present invention, the target prediction data at the next moment is obtained through a Kalman filter, and the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate act together to determine the radar final measurement data at the next moment, excluding the data within the non-detection accuracy of the current target and other interference data in the networked radar, so that the radar final measurement data at the next moment can be used as the optimal data to participate in the determination of the trajectory of the current target. In addition, by performing trajectory fusion on the multiple tracking trajectories of all radars through a convex combination data fusion algorithm, the quality and efficiency of the overall tracking trajectory generation are improved.

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Description of the Drawings

[0044] Figure 1 It is a schematic flowchart of a multi-target adaptive association tracking method for a networking radar provided by an embodiment of the present invention;

[0045] Figure 2 It is a simulation result diagram of an adaptive tracking gate during the tracking process of an FPS-108 early warning radar provided by an embodiment of the present invention;

[0046] Figure 3 It is a simulation diagram of a tracking gate of an FPS-108 radar under deceptive jamming provided by an embodiment of the present invention;

[0047] Figure 4 It is a comparison diagram of the root mean square error after fusion of target 1 and the root mean square errors of three radars without fusion provided by an embodiment of the present invention;

[0048] Figure 5 It is a schematic structural diagram of a multi-target adaptive association tracking device for a networking radar provided by an embodiment of the present invention;

[0049] Figure 6 It is a schematic diagram of a multi-target adaptive association tracking system for a networking radar provided by an embodiment of the present invention. Detailed Embodiments

[0050] In the present invention, the networking radar expands the detection range of the target through overall planning and reasonable layout of multiple radars. However, within the radar network, the measurement information of the radars often deviates from the true position of the target, and the measurement errors of different radars are different, which will lead to a reduction in the radar tracking accuracy. Compared with the traditional tracking algorithm, the present invention designs an adaptive tracking gate for the measurement errors of different radars within the network, thereby improving the tracking accuracy.

[0051] The following further describes the present invention in detail in conjunction with specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0052] In order to improve the generation quality and efficiency of the tracking trajectory, an embodiment of the present invention provides a multi-target adaptive association tracking method for a networking radar. Figure 1 It is a schematic flowchart of a multi-target adaptive association tracking method for a networking radar provided by an embodiment of the present invention, as Figure 1 shown, including:

[0053] S101. Calculate the motion state of the target at the current moment according to the final radar measurement data at the current moment and the final radar measurement data at the previous moment.

[0054] In an embodiment of the present invention, it is assumed that in a three-dimensional plane, there are n (n = 1, 2,..., N) radars and m (m = 1, 2,..., M) targets moving in a uniform straight line. Then, for the measurement data of the targets observed by the j-th (1 ≤ j ≤ N) radar at time k, it is as follows:

[0055]

[0056] where represents the measurement error of the sensor observed by the j-th radar, is the true trajectory of the target at time k, where is the motion state of the target at time k. If in a uniform state can be expressed as where x k , y k , and z k , respectively represent the position and velocity of the target in the X, Y, and Z axis directions at time k. is the measurement matrix, which is used to map the implicit true state space to the observation space. Therefore, in the actual measurement data of the radar, there are always measurement errors

[0057] In an embodiment of the present invention, the final measurement data of the radar is the measurement data that is closest to the target prediction data and meets the adaptive tracking gate after being processed according to S101 - S104 from the measurement data of multiple radars, and only corresponds to one optimal data. Correspondingly, the measurement data is the initial data of the radar for the current target, which is unfiltered data and includes the data obtained by multiple radars.

[0058] Optionally, according to the final measurement data of the radar at the current moment and the final measurement data of the radar at the previous moment, calculate the motion state of the target at the current moment, including:

[0059] Obtain the position information of the target at the current moment according to the final measurement data of the radar at the current moment;

[0060] Subtract the final measurement data of the radar at the current moment from the final measurement data of the radar at the previous moment, and divide by the time difference between the current moment and the previous moment to obtain the velocity information of the target at the current moment;

[0061] Take the position information of the target at the current moment and the velocity information of the target at the current moment as the motion state of the target at the current moment.

[0062] S102. Input the motion state of the target at the current moment into the Kalman filter to obtain the target prediction data at the next moment.

[0063] S103. Determine the tracking gate error according to the final radar measurement data at the previous moment and the radar's own error information, and sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment.

[0064] Optionally, the radar's own error information includes: range error, azimuth error, elevation angle error, and combined error;

[0065] Among them, the combined error is the combined information including range error, azimuth error, and elevation angle error.

[0066] Optionally, determining the tracking gate error according to the final radar measurement data at the previous moment and the radar's own error information, and summing the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment includes:

[0067] Sum the final radar measurement data at the previous moment with the range error, azimuth error, elevation angle error, and combined error respectively to obtain multiple intermediate measurement data;

[0068] Calculate the Euclidean distances between the intermediate measurement data and the final radar measurement data at the previous moment respectively to obtain multiple initial tracking gate errors;

[0069] Sort the multiple initial tracking gate errors by magnitude, and obtain the tracking gate error according to the sorting result;

[0070] Sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment.

[0071] Optionally, in the embodiments of the present invention, sorting the multiple initial tracking gate errors by magnitude and obtaining the tracking gate error according to the sorting result includes:

[0072] Take the initial tracking gate error with the largest value in the sorting result as the tracking gate error.

[0073] It should be noted that, in the embodiments of the present invention, due to the different types of radars in the networked radar, the radar's own error information is also different. And the own error error of radar j generally exists in: range error or azimuth error or elevation angle error or combined error (including range error, azimuth error, and elevation angle error).

[0074] Specifically, when radar j only has a range error, that is its initial tracking gate error γ error1 can be expressed as:

[0075]

[0076] Among them, Z k represents the final measurement data at the current moment, and can also be expressed in polar coordinates as where R k , θ k , respectively represent the distance, azimuth angle, and elevation angle at time k.

[0077] When the above formula is converted to rectangular coordinates, it can be expressed as:

[0078]

[0079] When radar j only has an azimuth error, that is its initial tracking gate error γ error2 can be expressed as:

[0080]

[0081] When it is converted to rectangular coordinates, it can be expressed as:

[0082]

[0083] When the radar only has an elevation error, that is its initial tracking gate error γ error3 can be expressed as:

[0084]

[0085] When it is converted to rectangular coordinates, it can be expressed as:

[0086]

[0087] When the radar has range error, azimuth error, and elevation error at the same time, that is its initial tracking gate error γ error4 can be expressed as;

[0088]

[0089] When it is converted to rectangular coordinates, it can be expressed as:

[0090]

[0091] Sort multiple initial tracking gate errors according to their magnitudes, and obtain the tracking gate error γ error , including:

[0092] γ error = max[γerror1 , γ error2 , γ error3 , γ error4 ;

[0093] Therefore, the adaptive tracking gate γ at the next moment k+1 can be expressed as:

[0094] γ k+1 = γ k + γ error ;

[0095] It should be noted that in this embodiment, when there is interference in the environment, the measurement error of the radar will increase correspondingly, which will also cause the adaptive tracking gate to change. When suppressing interference on the radar, the detection range of the radar will be reduced. When applying deceptive interference, it will affect the detection accuracy of the radar in terms of distance and angle, etc., and then cause the adaptive tracking gate to change.

[0096] S104. Input the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final measurement data of the radar at the next moment.

[0097] Optionally, in the embodiment of the present invention, inputting the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final measurement data of the radar at the next moment includes:

[0098] The radar Euclidean distance formula is expressed as:

[0099]

[0100] where is the radar Euclidean distance, k represents the current moment, j represents the jth radar, where j takes values of 1 ≤ j ≤ N, and N is the total number of radars, is the measurement data of the target at the next moment, is the target prediction data at the next moment, γ k+1 is the adaptive tracking gate at the next moment.

[0101] It should be noted that in the embodiment of the present invention, by using the Euclidean distance formula in the measurement data of the target at the next moment measured by multiple radars, the final measurement data of the radar at the next moment that meets the conditions and is optimal is determined,

[0102] It should be noted that needs to be fully satisfied formula, and is closest to the Euclidean distance.

[0103] In addition, in the embodiment of the present invention, when it is determined that after that, by associating with the track at the k-th moment, the motion state at the (k + 1)-th moment is updated.

[0104] Specifically, x k+1 , y k+1 , and z k+1 , represent the position and velocity of the target in the X, Y, and Z axis directions respectively at the (k + 1)-th moment.

[0105]

[0106]

[0107]

[0108] Among them, x k , y k , z k respectively represent the coordinate information of the target in the X, Y, and Z axis directions at the k-th moment, and x k+1 , y k+1 , z k +1 respectively represent the coordinate information of the target in the X, Y, and Z axis directions at the (k + 1)-th moment. Further, x k+1 , y k+1 , z k+1 can be obtained from the final measurement data at the (k + 1)-th moment. t k+1 represents the time corresponding to the (k + 1)-th moment, and t k represents the time corresponding to the k-th moment.

[0109] S105. Connect the radar final measurement data at the next moment of the current radar with the radar final measurement data at the previous moment to obtain a plurality of tracking trajectories.

[0110] S106. Perform trajectory fusion on the plurality of tracking trajectories of all radars through the convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target.

[0111] An embodiment of the present invention provides a method for multi-target adaptive association and tracking of a networking radar, including: calculating the motion state of a target at the current moment according to the final radar measurement data at the current moment and the final radar measurement data at the previous moment; inputting the motion state of the target at the current moment into a Kalman filter to obtain the target prediction data at the next moment; determining the tracking gate error according to the final radar measurement data at the previous moment and the radar's own error information, and summing the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment; inputting the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment; connecting the final radar measurement data at the next moment of the current radar with the final radar measurement data at the previous moment to obtain multiple tracking trajectories; fusing the multiple tracking trajectories of all radars through a convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target. In the embodiment of the present invention, the target prediction data at the next moment is obtained through a Kalman filter, and the final radar measurement data at the next moment is determined by the combined action of the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate, excluding the data within the non-detection accuracy of the current target and other interference data in the networking radar, so that the final radar measurement data at the next moment can be used as the optimal data to participate in the determination of the trajectory of the current target. In addition, by fusing the multiple tracking trajectories of all radars through a convex combination data fusion algorithm, the quality and efficiency of the overall tracking trajectory generation are improved.

[0112] Optionally, in the embodiment of the present invention, before inputting the motion state of the target at the current moment into a Kalman filter to obtain the target prediction data at the next moment, a method for multi-target adaptive association and tracking of a networking radar further includes:

[0113] Obtaining the initial track of the target according to the final radar measurement data at the two previous moments of the current moment; initializing the Kalman filter using the initial track.

[0114] It should be noted that in the embodiment of the present invention, the intuitive method and the final radar measurement data at the two previous moments of the current moment are used to start the track. The intuitive method is implemented by establishing a gate within the radar sampling time with the starting point as the center, and the range of motion with the maximum and minimum speeds. When the point detected at the current moment falls within this gate, it indicates that this point and the starting point may be on the same track.

[0115] Initializing the Kalman filter using the initial track is mainly applicable to initializing the direction of the track.

[0116] To more comprehensively demonstrate the performance improvement of a multi-network radar multi-target adaptive association and tracking method provided by the present invention, simulation experiments are also conducted in the embodiments of the present invention.

[0117] Simulation content:

[0118] Ten targets are tracked according to various warning radars in the network, and their tracking trajectories and root mean square errors of the trajectories are simulated. At the same time, the adaptive tracking gates at different times are also simulated.

[0119] Simulation: There are three warning radars, namely FPS-132, FPS-108, and LRDR, in the network. Figure 2 The adaptive tracking gate is simulated during the tracking process of the FPS-108 warning radar. It can be seen from Figure 2 that the adaptive tracking gate is constantly changing with the change of time and the relative distance between the target and the radar. When the distance between the radar and the target is close, the measurement error of the radar is small, so the adaptive tracking gate is also small. When the distance between the radar and the target gradually becomes farther, the adaptive tracking gate also gradually becomes larger.

[0120] When there is interference during the radar tracking process, it also affects the measurement accuracy of the radar itself. Therefore, for the two cases of barrage jamming and deception jamming, the tracking situation of the radar is simulated.

[0121] Simulation parameter design:

[0122] Assume that there is a type of deception jamming respectively. When the detection probability is 0.9, the detection accuracy of the radar is interfered.

[0123] Simulation: Under deception jamming, Figure 3 This is the simulation diagram of the tracking gate of the FPS-108 radar. It can be seen from Figure 3 that under deception jamming, the angle measurement and ranging accuracy errors will increase, which will also cause the adaptive tracking gate to change.

[0124] The simulation of the target trajectory fusion observed by each radar in the networked radar is carried out. There are 10 targets and 13 radars in the networked radar system.

[0125] Simulation: For the tracking trajectory fusion of 10 targets by 13 radars, Figure 4 This is the comparison diagram of the root mean square error after fusion of Target 1 provided by the embodiment of the present invention and the root mean square errors of the three radars without fusion. It can be seen from Figure 4 that after the fusion of multiple radars (the present invention), the root mean square error of the trajectory is greatly reduced, and the tracking quality is greatly improved.

[0126] The method provided by the embodiments of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc. There is no limitation here. Any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0127] Based on the same inventive concept, the embodiments of the present invention also provide a multi-target adaptive association and tracking device for a networked radar. Figure 5 It is a schematic structural diagram of a multi-target adaptive association and tracking device for a networked radar provided by the embodiments of the present invention. As Figure 5 shown, it includes: a calculation unit 601, an acquisition unit 602, and a fusion unit 603.

[0128] The calculation unit 601 is configured to calculate the motion state of the target at the current moment according to the final radar measurement data at the current moment and the final radar measurement data at the previous moment.

[0129] The acquisition unit 602 is configured to input the motion state of the target at the current moment into a Kalman filter to obtain the target prediction data at the next moment.

[0130] The calculation unit 601 is further configured to determine the tracking gate error according to the final radar measurement data at the previous moment and the radar's own error information, and sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment.

[0131] The calculation unit 601 is further configured to input the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment.

[0132] The fusion unit 603 is configured to connect the final radar measurement data at the next moment of the current radar with the final radar measurement data at the previous moment to obtain multiple tracking trajectories.

[0133] The fusion unit 603 is further configured to perform trajectory fusion on the multiple tracking trajectories of all radars through a convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target.

[0134] Figure 6 It is a schematic diagram of a multi-target adaptive association and tracking system for a networked radar provided by the embodiments of the present invention, including: a processor 710, a storage medium 720, and a bus 730. The storage medium 720 stores machine-readable instructions executable by the processor 710. When the multi-target adaptive association and tracking system for a networked radar runs, the processor 710 communicates with the storage medium 720 through the bus 730, and the processor 710 executes the machine-readable instructions to perform the steps of the above method embodiments. The specific implementation manners and technical effects are similar and will not be elaborated here.

[0135] The storage medium may include a Random Access Memory (RAM), or may also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the storage medium may also be at least one storage device located away from the aforementioned processor.

[0136] The aforementioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0137] The present invention also provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of any of the above-mentioned multi-target adaptive association tracking methods for networking radars are implemented.

[0138] In the description of this specification, the description with reference to terms such as "an embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0139] Although the present invention is described herein in connection with various embodiments, however, in the process of implementing the claimed invention, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the accompanying drawings and the disclosure content. In the description of the present invention, the term "including" does not exclude other components or steps, the term "one" or "a" does not exclude a plurality of cases, and the meaning of "a plurality" is two or more, unless otherwise specifically defined. In addition, certain measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0140] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. An adaptive multi-target association and tracking method for a networking radar, characterized in that Including: Calculate the motion state of the target at the current moment based on the final radar measurement data at the current moment and the final radar measurement data at the previous moment; Input the motion state of the target at the current moment into the Kalman filter to obtain the target prediction data at the next moment; Determine the tracking gate error according to the final radar measurement data at the previous moment and the radar's own error information, and sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment; Input the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment; Connect the final radar measurement data at the next moment of the current radar with the final radar measurement data at the previous moment to obtain multiple tracking trajectories; Fuse the multiple tracking trajectories of all radars through the convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target.

2. The multi-target adaptive association and tracking method for a networking radar according to claim 1, characterized in that The calculating the motion state of the target at the current moment according to the final radar measurement data at the current moment and the final radar measurement data at the previous moment includes: Obtain the position information of the target at the current moment according to the final radar measurement data at the current moment; Subtract the final radar measurement data at the previous moment from the final radar measurement data at the current moment, and divide the result by the time difference between the current moment and the previous moment to obtain the velocity information of the target at the current moment; Use the position information of the target at the current moment and the velocity information of the target at the current moment as the motion state of the target at the current moment.

3. A multi-target adaptive association and tracking method for a networking radar according to claim 1, characterized in that, The radar's own error information includes: range error, azimuth error, elevation angle error, and combined error; Wherein, the combined error is the combined information including range error, azimuth error, and elevation angle error.

4. A multi-target adaptive association and tracking method for a networking radar according to claim 3, characterized in that The determining the tracking gate error according to the final radar measurement data at the previous moment and the radar's own error information, and summing the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment includes: Sum the final radar measurement data at the previous moment with the range error, the azimuth error, the elevation angle error, and the combined error respectively to obtain multiple intermediate measurement data; Calculate the Euclidean distance between each intermediate measurement data and the final radar measurement data at the previous moment to obtain multiple initial tracking gate errors; Sort the multiple initial tracking gate errors according to their magnitudes, and obtain the tracking gate error according to the sorting result; Sum the tracking gate error with the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment.

5. A multi-target adaptive association and tracking method for a networking radar according to claim 4, characterized in that The sorting the multiple initial tracking gate errors according to their magnitudes and obtaining the tracking gate error according to the sorting result includes: Use the initial tracking gate error with the largest value in the sorting result as the tracking gate error.

6. The multi-target adaptive association tracking method for a networking radar according to claim 1, wherein Input the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment, including: The radar Euclidean distance formula is expressed as: Among them, is the radar Euclidean distance, k represents the current moment, j represents the j-th radar, where j takes values from 1 ≤ j ≤ N, and N is the total number of radars. is the measurement data of the target at the next moment. is the predicted data of the target at the next moment, γ k+1 is the adaptive tracking gate at the next moment.

7. A multi-target adaptive association and tracking method for a networking radar according to claim 1, characterized in that Before inputting the motion state of the target at the current moment into the Kalman filter to obtain the target prediction data at the next moment, the multi-target adaptive association tracking method for a networked radar further includes: Obtain the initial track of the target according to the final radar measurement data at the previous two moments of the current moment; Initialize the Kalman filter using the initial track.

8. An adaptive multi-target association and tracking device for a networking radar, characterized in that Including: A calculation unit, an acquisition unit, and a fusion unit; The calculation unit is configured to calculate the motion state of the target at the current moment according to the final radar measurement data at the current moment and the final radar measurement data at the previous moment; The acquisition unit is configured to input the motion state of the target at the current moment into the Kalman filter to obtain the target prediction data at the next moment; The calculation unit is further configured to determine the tracking gate error according to the final radar measurement data at the previous moment and the radar self-error information, and sum the tracking gate error and the adaptive tracking gate at the current moment to obtain the adaptive tracking gate at the next moment; The calculation unit is further configured to input the target prediction data at the next moment, the measurement data of the target at the next moment, and the adaptive tracking gate at the next moment into the radar Euclidean distance formula to determine the final radar measurement data at the next moment; The fusion unit is configured to connect the final radar measurement data at the next moment of the current radar with the final radar measurement data at the previous moment to obtain multiple tracking trajectories; The fusion unit is further configured to perform trajectory fusion on the multiple tracking trajectories of all radars through the convex combination data fusion algorithm to obtain the tracking fusion trajectory of the current target.

9. An adaptive multi-target association and tracking system for a networking radar, characterized in that Including: A processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the multi-target adaptive association tracking system for a networked radar runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the method according to any one of claims 1-7.

10. A storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is run by the processor, it performs the steps of the method according to any one of claims 1-7.

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