Target type recognition method, system, device and medium based on radar detection data

Abstract

The present application discloses a target type identification method, system, device and medium based on radar detection data, which relates to the field of space target identification technology. The method includes: obtaining the initial radar detection data to be identified, and evenly dividing the initial radar detection data into several segments according to the time length, and determining the motion characteristic parameters of the space target corresponding to each radar detection data segment according to the track quadratic fitting function; then correlating and fusing the tracks of each space target according to the time when each space target appears, and finally analyzing the motion characteristics of the track of each space target to identify the target type of the space target and output it. The present application uses the track quadratic fitting function obtained by pre-fitting to perform track extrapolation comparison and integration of multiple space target tracks, and then identifies the type of space target through track motion characteristic analysis, which can assist in judging the correctness of radar tracking and provide auxiliary decision-making for the correct execution of tracking plans.

Description

Technical Field

[0001] The present application relates to the technical field of space target recognition, and in particular to a target type recognition method, system, device and medium based on radar detection data. Background Art

[0002] With the booming commercial space industry, many companies are seeking to independently produce satellites and build their own satellite networks. The launch and placement of satellites into orbit rely on rockets, which carry potential risks. Therefore, real-time monitoring of the entire launch process is crucial to ensure the smooth progress of the mission.

[0003] Radar is a common tool for monitoring rocket launches, tracking a rocket's trajectory within a specific area. During flight, a rocket undergoes critical maneuvers such as booster separation, fairing jettison, and satellite-rocket separation. These maneuvers often generate debris and debris that travel with the main rocket, interfering with radar detection. Furthermore, radar may also detect other space targets, such as satellites and aircraft, during detection. These factors can also interfere with radar tracking, leading to incorrect tracking.

[0004] Therefore, how to accurately identify the type of space target detected by radar has become a key issue that needs to be urgently addressed in this field. Summary of the Invention

[0005] The purpose of this application is to provide a target type identification method, system, device and medium based on radar detection data, which can accurately distinguish the type of space targets detected by radar to assist in determining the correctness of radar tracking.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for target type identification based on radar detection data, comprising the following steps:

[0008] Initial radar detection data to be identified is obtained, and the initial radar detection data is evenly divided into several segments according to time length to obtain several radar detection data segments.

[0009] For any segment of radar detection data, the motion characteristic parameters of the space target corresponding to the radar detection data segment are determined based on the track quadratic fitting function obtained in advance; the track quadratic fitting function is used to characterize the nonlinear relationship between the detection time of the radar detection data and the coordinates of the space target in the inertial coordinate system; the motion characteristic parameters include velocity, acceleration, altitude, gravitational acceleration and the normal vector of the plane where the track is located.

[0010] Based on the motion characteristic parameters of the space target corresponding to each segment of radar detection data, the tracks of each space target are correlated and fused according to the time when each space target appears, and the tracks of several space targets are obtained.

[0011] For the track of any space target, the motion characteristics are analyzed according to the track of the space target, and the target type of the space target is identified and output; the target type includes whether it is a space target in close space, whether it has power, whether it is a space target in random motion, and the size of the space target's motion ability.

[0012] Optionally, determining the motion characteristic parameters of the space target corresponding to the radar detection data segment based on the track quadratic fitting function obtained by pre-fitting specifically includes the following steps:

[0013] The time median of the radar detection data segment is taken, and the three-dimensional spatial coordinates of the space target corresponding to the radar detection data segment in the inertial coordinate system are determined according to the track quadratic fitting function obtained in advance.

[0014] The first-order and second-order derivatives of the three-dimensional spatial coordinates of the space target corresponding to the radar detection data segment are calculated respectively, and the speed, acceleration, height and gravitational acceleration of the space target corresponding to the radar detection data segment are determined in combination with the law of universal gravitation.

[0015] The three-dimensional spatial coordinates of several time points of the radar detection data segment are calculated, and the plane normal vector of the track of the space target corresponding to the radar detection data segment is determined based on the cross product between the three-dimensional spatial coordinate vectors of each pair of adjacent time points.

[0016] Optionally, calculating the three-dimensional spatial coordinates of a plurality of time points for the radar detection data segment, and determining the plane normal vector of the track of the space target corresponding to the radar detection data segment based on the cross product between the three-dimensional spatial coordinate vectors of each pair of adjacent time points, specifically includes the following steps:

[0017] For the radar detection data segment, a number of time points are taken at equal intervals within the time range of the radar detection data segment to obtain a time series.

[0018] For any time point, the three-dimensional space coordinates of the space target corresponding to the time point in the inertial coordinate system are determined according to the track quadratic fitting function obtained in advance.

[0019] For any two adjacent time points in the time series, the cross product of the two time points is calculated based on the three-dimensional spatial coordinates of the space targets corresponding to the two time points in the inertial coordinate system.

[0020] The average of all cross products calculated according to the time series is used to obtain the plane normal vector of the track of the space target corresponding to the radar detection data segment.

[0021] Optionally, the track quadratic fitting function is obtained in advance by fitting according to the following steps:

[0022] Radar detection data with a clear track is obtained, and the radar detection data with a clear track is evenly divided into a number of segments according to the time length to obtain a number of radar detection data segments with a clear track.

[0023] For any radar detection data segment with a clear track, the three-dimensional spatial coordinates of the corresponding space target in the inertial coordinate system are determined based on the radar installation position and the detection data in the radar detection data segment.

[0024] According to the time median of the radar detection data segment and the three-dimensional spatial coordinates of the corresponding space target in the inertial coordinate system, the track quadratic fitting function is obtained by the least squares method.

[0025] Optionally, the track quadratic fitting function is as follows:

[0026] .

[0027] in, x , y , z is the three-dimensional space coordinate of the space target in the inertial coordinate system, a x 、 a y 、 a z 、 b x 、 b y 、 b z 、 c x 、 c y 、 c z are the fitting coefficients, t The time point value of the radar detection data.

[0028] Optionally, based on the motion characteristic parameters of the space target corresponding to each radar detection data segment, the tracks of each space target are correlated and fused according to the time when each space target appeared, to obtain the tracks of several space targets, which specifically includes the following steps:

[0029] According to the time interval and time interval threshold range of the space targets corresponding to any two radar detection data segments, it is judged whether the space targets corresponding to the two radar detection data segments may be the same space target.

[0030] If the time interval between the appearances of the space targets corresponding to the two radar detection data segments is less than the minimum value of the time interval threshold range, it is determined that the space targets corresponding to the two radar detection data segments are not the same space target.

[0031] If the time interval between the appearances of the space targets corresponding to the two radar detection data segments is greater than the maximum value of the time interval threshold range, it is determined that the space targets corresponding to the two radar detection data segments are not the same space target.

[0032] If the time interval between the appearances of the space targets corresponding to the two radar detection data segments is within the time interval threshold, the tracks of the two space targets are extrapolated to the same time. When the comparison result of the motion characteristic parameters is less than the comparison error threshold, the tracks of the two space targets are correlated and fused. Repeat the above steps to obtain the tracks of several space targets.

[0033] Optionally, performing motion characteristic analysis based on the trajectory of the space target, identifying the target type of the space target and outputting the result, specifically includes the following steps:

[0034] The altitude and gravity acceleration of the space target can be used to preliminarily determine whether the space target is a near-space space target.

[0035] The speed and acceleration of the space target can be used to further determine whether the space target is a powered space target and the size of its movement ability.

[0036] The normal vector of the plane where the space target's track is located is used to determine whether the space target is a randomly moving space target.

[0037] In a second aspect, the present application provides a target type recognition system based on radar detection data, comprising the following functional modules:

[0038] The radar detection data acquisition and segmentation module is used to acquire the initial radar detection data to be identified and evenly divide the initial radar detection data into several segments according to the time length to obtain several radar detection data segments.

[0039] The space target motion characteristic parameter solving module is used to determine the motion characteristic parameters of the space target corresponding to any radar detection data segment based on the track quadratic fitting function obtained by pre-fitting. The track quadratic fitting function is used to characterize the nonlinear relationship between the detection time of the radar detection data and the coordinates of the space target in the inertial coordinate system. The motion characteristic parameters include velocity, acceleration, altitude, gravitational acceleration and the normal vector of the plane where the track is located.

[0040] The space target track judgment association fusion module is used to associate and fuse the tracks of each space target according to the motion characteristic parameters of each space target corresponding to each radar detection data segment and the time when each space target appears, so as to obtain the tracks of several space targets.

[0041] The space target motion characteristic analysis and identification module is used to analyze the motion characteristics of any space target based on the track of the space target, identify the target type of the space target and output it; the target type includes whether it is a space target in close space, whether it is a space target with power, whether it is a space target in random motion, and the size of the space target's motion ability.

[0042] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the target type identification method based on radar detection data described above.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the target type identification method based on radar detection data described above.

[0044] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0045] The present application provides a method, system, device and medium for target type identification based on radar detection data. The method includes: obtaining the initial radar detection data to be identified and evenly dividing the initial radar detection data into several segments according to the time length, and determining the motion characteristic parameters of the space target corresponding to each radar detection data segment based on the track quadratic fitting function obtained by pre-fitting; then correlating and fusing the tracks of each space target according to the time when each space target appears, and finally analyzing the motion characteristics of the track of each space target to identify the target type of the space target and output it. The present application processes the detection data in segments, uses the track quadratic fitting function obtained by pre-fitting to perform track extrapolation and comparison, integrates the tracks of multiple space targets, and then identifies the type of space target through the track motion characteristic analysis. This can assist in determining the correctness of radar tracking and provide auxiliary decision-making for the correct execution of the tracking plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 This is a diagram of the application environment of a target type recognition method based on radar detection data provided in one embodiment of the present application.

[0048] Figure 2 A flowchart of a target type identification method based on radar detection data provided in one embodiment of the present application.

[0049] Figure 3 This is a flowchart of step A2 in a target type identification method based on radar detection data provided in one embodiment of the present application.

[0050] Figure 4 This is a flowchart of step A23 in a target type recognition method based on radar detection data provided in one embodiment of the present application.

[0051] Figure 5 This is a flowchart of step A3 in a target type identification method based on radar detection data provided in one embodiment of the present application.

[0052] Figure 6 This is a flowchart of step A4 in a target type recognition method based on radar detection data provided in one embodiment of the present application.

[0053] Figure 7A flowchart of a track quadratic fitting function obtained by pre-fitting in a target type recognition method based on radar detection data provided in one embodiment of the present application.

[0054] Figure 8 A schematic diagram of the functional modules of a target type recognition system based on radar detection data provided in one embodiment of the present application.

[0055] Figure 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0058] The target type recognition method based on radar detection data provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers.

[0059] Terminal 102 can send initial radar detection data to be identified to server 104. After receiving the initial radar detection data, server 104 evenly divides the initial radar detection data into several segments based on their time length, generating a number of radar detection data segments. For each radar detection data segment, the motion characteristic parameters of the space target corresponding to the radar detection data segment are determined based on a pre-fitted quadratic track fitting function. The quadratic track fitting function characterizes the nonlinear relationship between the detection time of the radar detection data and the coordinates of the space target in the inertial coordinate system. The motion characteristic parameters include velocity, acceleration, altitude, gravitational acceleration, and the normal vector of the plane in which the track lies. Based on the motion characteristic parameters corresponding to the space target in each radar detection data segment, the tracks of each space target are correlated and fused according to the time of their appearance to obtain the tracks of several space targets. For each space target track, the motion characteristics of the space target are analyzed based on the track, and the target type of the space target is identified and output. Server 104 can provide the obtained space target number and target type to terminal 102.

[0060] Furthermore, in some embodiments, the target type identification method based on radar detection data can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly process the initial radar detection data to be identified, or server 104 can retrieve the initial radar detection data to be identified from a data storage system and process it. Terminal 102 can include, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, etc. Portable wearable devices can include smart watches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented as a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0061] In an exemplary embodiment, Figure 2 As shown, a target type recognition method based on radar detection data is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0062] A1. Obtain the initial radar detection data to be identified and evenly divide it into several segments according to the time length to obtain several radar detection data segments. Specifically, since the radar is affected by the external environment, target operation status, and self-generated errors during detection, there is a certain amount of accidental error in the data at a single point. Therefore, a sliding window segmentation method is used to obtain the value, combined with the time median of the subsequent segments. t To fit the independent variable of the function, the spatial coordinates can be solved and the motion characteristics can be calculated to eliminate the influence of the above accidental errors.

[0063] A2. For any radar detection data segment, determine the motion characteristic parameters of the space target corresponding to the radar detection data segment based on the track quadratic fitting function obtained in advance; the track quadratic fitting function is used to characterize the nonlinear relationship between the detection time of the radar detection data and the coordinates of the space target in the inertial coordinate system; the motion characteristic parameters include velocity, acceleration, altitude, gravity acceleration and the normal vector of the plane where the track is located. Specifically, Figure 3 As shown, step A2 includes the following steps:

[0064] A21. Take the temporal median of the radar detection data segment and, based on the pre-fitted quadratic track fitting function, determine the three-dimensional spatial coordinates of the space target corresponding to the radar detection data segment in the inertial coordinate system. The three-dimensional coordinates of the space target in the Earth-fixed coordinate system can be calculated using the radar installation position, the azimuth, elevation, detection range, and detection time in the detection data. However, due to the influence of the Earth's rotation, the continuous coordinate sequence in the Earth-fixed coordinate system is inevitably not in the same plane. Therefore, the coordinates are converted to inertial coordinates. In this embodiment, the J2000 inertial coordinate system is used.

[0065] Specifically, since the initial radar detection data is segmented using a sliding window method in step A1, only one time point value is taken as input for each segment. Here, the time point value in the middle of the segment is taken for calculation to reduce the impact of accidental errors.

[0066] In this embodiment, the track quadratic fitting function is shown as follows:

[0067] .

[0068] in, x , y , z is the three-dimensional space coordinate of the space target in the inertial coordinate system, a x 、 a y 、 a z 、 b x 、 by 、 b z 、 c x 、 c y 、 c z are the fitting coefficients, t The time point value of the radar detection data.

[0069] A22. Calculate the first-order and second-order derivatives of the three-dimensional coordinates of the space target corresponding to the radar detection data segment, and determine the velocity, acceleration, altitude and gravitational acceleration of the space target corresponding to the radar detection data segment in combination with the law of universal gravitation. x , y , z , The value of and the motion characteristic parameters of the space target at any time are solved by the universal gravitation.

[0070] A23. Calculate the three-dimensional spatial coordinates of several time points for the radar detection data segment, and determine the plane normal vector of the track of the space target corresponding to the radar detection data segment based on the cross product between the three-dimensional spatial coordinate vectors of each pair of adjacent time points. Specifically, Figure 4 As shown, step A23 includes the following steps:

[0071] A231. For the radar detection data segment, a number of time points are taken at equal intervals within the time range of the radar detection data segment to obtain a time series. t 1 and end time t n Interpolation is used to obtain a time series with equal intervals.

[0072] A232. For any time point, determine the three-dimensional space coordinates of the space target corresponding to the time point in the inertial coordinate system based on the track quadratic fitting function obtained in advance. Substitute the time points in the time series into the fitting function in turn and solve x , y , z , forming a coordinate vector sequence.

[0073] A233. For any two adjacent time points in a time series, calculate the cross product of the two time points based on the three-dimensional spatial coordinates of the corresponding space targets in the inertial coordinate system. Take two adjacent coordinate vectors in the coordinate sequence and calculate their cross products to obtain a cross product sequence.

[0074] A234. Average all cross products calculated based on the time series to obtain a normal vector for the plane containing the track of the space target corresponding to the radar detection data segment. Average all cross product values ​​in the cross product sequence as the normal vector for the plane containing the track of the space target corresponding to the radar detection data segment.

[0075] A3. Based on the motion characteristic parameters of the space targets corresponding to each segment of radar detection data, the tracks of each space target are correlated and fused according to the time when each space target appears, and the tracks of several space targets are obtained. Figure 5 As shown, step A3 specifically includes the following steps:

[0076] A31. Determine whether the space targets corresponding to any two radar detection data segments are likely to be the same space target based on the time interval and time interval threshold range between the space targets corresponding to the two radar detection data segments.

[0077] If the time interval between the appearances of the space target corresponding to the two radar detection data segments is less than the minimum value of the time interval threshold range, or if the time interval between the appearances of the space target corresponding to the two radar detection data segments is greater than the maximum value of the time interval threshold range, that is, the time interval between the appearances of the space target corresponding to the two radar detection data segments is not within the time interval threshold range, then step A32 is executed. If the time interval between the appearances of the space target corresponding to the two radar detection data segments is within the time interval threshold range, then step A33 is executed.

[0078] A32. Determine that the space targets corresponding to the two radar detection data segments are not the same space target.

[0079] A33. The tracks of the two space targets are extrapolated to the same time, and when the comparison result of the motion characteristic parameters is less than the comparison error threshold, the tracks of the two space targets are correlated and fused.

[0080] A34. Repeat the judgment in step A31 above until the matching judgment of all radar detection data segments is completed and the tracks of several space targets are obtained.

[0081] A4. For any space target's trajectory, analyze its motion characteristics based on the target's trajectory, identify its target type, and output the result. Target types include whether it is a near-space target, whether it is powered, whether it is randomly moving, and the size of the target's motion capability.

[0082] Specifically, if Figure 6 As shown, step A4 includes the following steps:

[0083] A41. Based on the altitude and gravity acceleration of the space target, a preliminary judgment is made as to whether the space target is a near-space target.

[0084] A42. The speed and acceleration of the space target are used to further determine whether the space target is a powered space target and the size of its movement capability.

[0085] A43. Determine whether the space target is a randomly moving space target based on the normal vector of the plane where the space target's track lies.

[0086] In an exemplary embodiment of the present application, a process of fitting in advance to obtain a quadratic fitting function of the track is also included, such as Figure 7 As shown, the process includes the following steps:

[0087] B1. Obtain radar detection data with a clear track, and evenly divide the radar detection data with a clear track into several segments according to time length to obtain several radar detection data segments with a clear track.

[0088] B2. For any radar detection data segment with a clear track, determine the three-dimensional spatial coordinates of the corresponding space target in the inertial coordinate system based on the radar installation position and the detection data in the radar detection data segment.

[0089] B3. Based on the time median of the radar detection data segment and the three-dimensional spatial coordinates of the corresponding space target in the inertial coordinate system, a track quadratic fitting function is obtained by fitting in a least squares manner.

[0090] The above-mentioned target type identification method based on radar detection data proposed in this application, by segmenting the detection data, using least squares segmented fitting to calculate the track motion characteristics, extrapolating and comparing the fitting function to integrate the multi-target tracks, and then identifying the target type through the motion characteristics of the track, can assist in judging the correctness of radar tracking and provide auxiliary decision-making for the correct execution of the tracking plan.

[0091] Based on the same inventive concept, embodiments of the present application also provide a system for implementing the aforementioned method for identifying target types based on radar detection data. The solution provided by this system is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more system embodiments provided below can be found in the aforementioned limitations for the method for identifying target types based on radar detection data, and will not be further elaborated here.

[0092] In an exemplary embodiment, Figure 8 As shown, a target type recognition system based on radar detection data is provided, including the following functional modules:

[0093] The radar detection data acquisition and segmentation module is used to acquire the initial radar detection data to be identified and evenly divide the initial radar detection data into several segments according to the time length to obtain several radar detection data segments.

[0094] The space target motion characteristic parameter solving module is used to determine the motion characteristic parameters of the space target corresponding to any radar detection data segment based on the track quadratic fitting function obtained by pre-fitting. The track quadratic fitting function is used to characterize the nonlinear relationship between the detection time of the radar detection data and the coordinates of the space target in the inertial coordinate system. The motion characteristic parameters include velocity, acceleration, altitude, gravitational acceleration and the normal vector of the plane where the track is located.

[0095] The space target track judgment association fusion module is used to associate and fuse the tracks of each space target according to the motion characteristic parameters of each space target corresponding to each radar detection data segment and the time when each space target appears, so as to obtain the tracks of several space targets.

[0096] The space target motion characteristic analysis and identification module is used to analyze the motion characteristics of any space target based on the track of the space target, identify the target type of the space target and output it; the target type includes whether it is a space target in close space, whether it is a space target with power, whether it is a space target in random motion, and the size of the space target's motion ability.

[0097] certainly, Figure 8 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Figure 8 One or at least two components of the system shown.

[0098] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the target type recognition method based on radar detection data provided in the above embodiment can be implemented.

[0099] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0100] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0101] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0102] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0103] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0104] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0105] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0106] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A target type recognition method based on radar detection data, characterized in that: include: Acquire initial radar detection data to be identified, and evenly divide the initial radar detection data into a plurality of segments according to time length to obtain a plurality of radar detection data segments; For any segment of radar detection data, the motion characteristic parameters of the space target corresponding to the radar detection data segment are determined based on the track quadratic fitting function obtained by pre-fitting. The track quadratic fitting function is used to characterize the nonlinear relationship between the detection time of the radar detection data and the coordinates of the space target in the inertial coordinate system. The motion characteristic parameters include velocity, acceleration, altitude, gravitational acceleration, and the normal vector of the plane in which the track is located. Based on the motion characteristic parameters of the space targets corresponding to each segment of radar detection data, the tracks of each space target are correlated and fused according to the time when each space target appeared, and the tracks of several space targets are obtained; For the track of any space target, the motion characteristics of the space target are analyzed according to the track of the space target, and the target type of the space target is identified and output; the target type includes whether it is a space target in near space, whether it has power, whether it is a space target in random motion, and the size of the space target's motion capability.

2. The target type identification method based on radar detection data according to claim 1, characterized in that: Determining the motion characteristic parameters of the space target corresponding to the radar detection data segment based on the track quadratic fitting function obtained in advance includes: Taking the time median of the radar detection data segment, and determining the three-dimensional spatial coordinates of the space target corresponding to the radar detection data segment in the inertial coordinate system based on the track quadratic fitting function obtained by pre-fitting; Calculating first-order and second-order derivatives of the three-dimensional spatial coordinates of the space target corresponding to the radar detection data segment, and determining the velocity, acceleration, altitude, and gravitational acceleration of the space target corresponding to the radar detection data segment in combination with the law of universal gravitation; The three-dimensional spatial coordinates of several time points are calculated for the radar detection data segment, and the plane normal vector of the track of the space target corresponding to the radar detection data segment is determined based on the cross product between the three-dimensional spatial coordinate vectors of each pair of adjacent time points.

3. The target type identification method based on radar detection data according to claim 2, characterized in that: Calculating the three-dimensional spatial coordinates of a plurality of time points for the radar detection data segment, and determining the plane normal vector of the track of the space target corresponding to the radar detection data segment based on the cross product between the three-dimensional spatial coordinate vectors of each pair of adjacent time points, specifically including: For the radar detection data segment, a number of time points are taken at equal intervals within the time range of the radar detection data segment to obtain a time series; For any time point, the three-dimensional space coordinates of the space target corresponding to the time point in the inertial coordinate system are determined according to the track quadratic fitting function obtained in advance; For any two adjacent time points in the time series, the cross product of the two time points is calculated based on the three-dimensional spatial coordinates of the space targets corresponding to the two time points in the inertial coordinate system; All cross products calculated according to the time series are averaged to obtain the plane normal vector of the track of the space target corresponding to the radar detection data segment.

4. The target type identification method based on radar detection data according to claim 1, characterized in that: The track quadratic fitting function is obtained by pre-fitting according to the following steps: Obtain radar detection data with clear tracks, and evenly divide the radar detection data with clear tracks into several segments according to the length of time, to obtain several segments of radar detection data with clear tracks For any radar detection data segment with a clear track, determine the three-dimensional spatial coordinates of the corresponding space target in the inertial coordinate system based on the radar installation position and the detection data in the radar detection data segment; According to the time median of the radar detection data segment and the three-dimensional space coordinates of the corresponding space target in the inertial coordinate system, a track quadratic fitting function is obtained by fitting in a least squares manner.

5. The target type identification method based on radar detection data according to claim 1, characterized in that: The track quadratic fitting function is shown as follows: ； in, x , y , z is the three-dimensional space coordinate of the space target in the inertial coordinate system, a x 、 a y 、 a z 、 b x 、 b y 、 b z 、 c x 、 c y 、 c z are the fitting coefficients, t The time point value of the radar detection data.

6. The target type identification method based on radar detection data according to claim 1, characterized in that: Based on the motion characteristic parameters of the space targets corresponding to each segment of radar detection data, the tracks of each space target are correlated and fused according to the time when each space target appears, and the tracks of several space targets are obtained, including: According to the time interval and time interval threshold range of the space targets corresponding to any two radar detection data segments, it is determined whether the space targets corresponding to the two radar detection data segments are likely to be the same space target; If the time interval between the appearance of the space targets corresponding to the two radar detection data segments is less than the minimum value of the time interval threshold range, it is determined that the space targets corresponding to the two radar detection data segments are not the same space target; If the time interval between the appearance of the space targets corresponding to the two radar detection data segments is greater than the maximum value of the time interval threshold range, it is determined that the space targets corresponding to the two radar detection data segments are not the same space target; If the time interval between the appearances of the space targets corresponding to the two radar detection data segments is within the time interval threshold, the tracks of the two space targets are extrapolated to the same time, and when the comparison result of the motion characteristic parameters is less than the comparison error threshold, the tracks of the two space targets are correlated and fused. The above steps are repeated to obtain the tracks of several space targets.

7. The target type identification method based on radar detection data according to claim 1, characterized in that: Performing motion characteristic analysis based on the track of the space target, identifying the target type of the space target and outputting the result, specifically includes: Preliminarily determine whether the space target is a near-space target based on the altitude and gravity acceleration of the space target; Further determining whether the space target is a powered space target and the size of its motion capability based on the speed and acceleration of the space target; Whether the space target is a randomly moving space target is determined by using the normal vector of the plane where the track of the space target is located.

8. A target type recognition system based on radar detection data, characterized in that: include: A radar detection data acquisition and segmentation module is used to acquire initial radar detection data to be identified and evenly divide the initial radar detection data into several segments according to the time length to obtain several radar detection data segments; A space target motion characteristic parameter solving module is used to determine the motion characteristic parameters of the space target corresponding to any radar detection data segment based on a pre-fitted track quadratic fitting function. The track quadratic fitting function is used to characterize the nonlinear relationship between the detection time of the radar detection data and the coordinates of the space target in the inertial coordinate system. The motion characteristic parameters include velocity, acceleration, altitude, gravitational acceleration, and the normal vector of the plane in which the track lies. The space target track judgment association fusion module is used to associate and fuse the tracks of each space target according to the motion characteristic parameters of each space target in each radar detection data segment and the time when each space target appears, so as to obtain the tracks of several space targets; The space target motion characteristic analysis and identification module is used to analyze the motion characteristics of any space target based on the track of the space target, identify the target type of the space target and output it; the target type includes whether it is a space target in close space, whether it is a space target with power, whether it is a space target in random motion, and the size of the space target's motion ability.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the target type recognition method based on radar detection data according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the target type recognition method based on radar detection data according to any one of claims 1 to 7 is implemented.

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