Target type identification method, system and equipment based on radar detection data and medium

Abstract

The invention discloses a target type identification method, system and device based on radar detection data, and a medium, and relates to the technical field of space target identification, and the method comprises the steps: obtaining to-be-identified initial radar detection data, uniformly dividing the initial radar detection data into a plurality of segments according to the time duration, determining motion characteristic parameters of a space target corresponding to each radar detection data segment according to a track quadratic fitting function; and then according to the occurrence time of each space target, association fusion is carried out on the track of each space target, finally, motion characteristic analysis is carried out on the track of each space target, and the target type of the space target is identified and output. According to the method, the flight path extrapolation comparison is performed by using the flight path quadratic fitting function obtained by fitting in advance, the flight paths of the multiple space targets are integrated, and the type of the space targets is identified by analyzing the motion characteristics of the flight paths, so that the correctness of radar tracking can be judged in an auxiliary manner, and an auxiliary decision-making effect is provided for correctly executing a tracking plan.

Description

Technical Field

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

[0002] With the booming development of the commercial space industry, numerous enterprises are seeking to independently produce satellites and build their own satellite networks. The launch and orbital injection of satellites rely on rockets, and there are potential risks during the rocket launch process. Therefore, it is crucial to monitor the entire launch process in real time to ensure the smooth progress of the mission.

[0003] Radar is a commonly used tool for monitoring rocket launches, and it can track the flight trajectory of a rocket within a specific area. During flight, the rocket will undergo key actions such as booster separation, fairing jettisoning, and satellite-rocket separation, which are often accompanied by the generation of debris and fragments. These fragments will fly along with the rocket body, thereby interfering with the radar detection. In addition, the radar may also capture other space targets such as satellites and aircraft during the detection process, and these factors will also interfere with the radar tracking, resulting in tracking of incorrect targets.

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

[0005] The purpose of the present application is to provide a method, system, device and medium for identifying target types based on radar detection data, which can accurately determine the type of space target detected by radar to assist in judging the correctness of radar tracking.

[0006] To achieve the above purpose, the present application provides the following solutions: In the first aspect, the present application provides a method for identifying target types based on radar detection data, including the following steps: Obtain 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 segments of radar detection data fragments.

[0007] For any segment of radar detection data fragment, determine the motion characteristic parameters of the space target corresponding to the radar detection data fragment according to the pre-fitted track quadratic fitting function; the track quadratic fitting function is used to characterize the non-linear 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 speed, acceleration, altitude, gravitational acceleration, and the normal vector of the plane where the track is located.

[0008] Based on the motion characteristic parameters of the spatial targets corresponding to each radar detection data segment, the tracks of each spatial target are associated and fused according to the appearance time of each spatial target, and the tracks of several spatial targets are obtained.

[0009] For the track of any spatial target, perform motion characteristic analysis according to the track of the spatial target, identify the target type of the spatial target and output it; the target type includes whether it is a near-space spatial target, whether it has power, whether it is a randomly moving spatial target, and the magnitude of the motion ability of the spatial target.

[0010] Optionally, according to the track quadratic fitting function obtained by pre-fitting, determine the motion characteristic parameters of the spatial target corresponding to the radar detection data segment, specifically including the following steps: Take the time median of the radar detection data segment, and according to the track quadratic fitting function obtained by pre-fitting, determine the three-dimensional spatial coordinates of the spatial target corresponding to the radar detection data segment in the inertial coordinate system.

[0011] Calculate the first derivative and the second derivative of the three-dimensional spatial coordinates of the spatial target corresponding to the radar detection data segment respectively, and combine the law of universal gravitation to determine the speed, acceleration, altitude and gravitational acceleration of the spatial target corresponding to the radar detection data segment.

[0012] Perform three-dimensional spatial coordinate calculations for several time points of the radar detection data segment, and determine the normal vector of the plane where the track of the spatial target corresponding to the radar detection data segment is located according to the cross product between the three-dimensional spatial coordinate vectors of each pair of adjacent two time points.

[0013] Optionally, perform three-dimensional spatial coordinate calculations for several time points of the radar detection data segment, and determine the normal vector of the plane where the track of the spatial target corresponding to the radar detection data segment is located according to the cross product between the three-dimensional spatial coordinate vectors of each pair of adjacent two time points, specifically including the following steps: For the radar detection data segment, take several time points at equal intervals within the time range of the radar detection data segment to obtain a time series.

[0014] For any time point, according to the track quadratic fitting function obtained by pre-fitting, determine the three-dimensional spatial coordinates of the spatial target corresponding to the time point in the inertial coordinate system.

[0015] For any two adjacent time points in the time series, calculate the cross product of the two time points according to the three-dimensional spatial coordinates of the spatial target corresponding to the two time points in the inertial coordinate system.

[0016] Calculate the average value of all cross products calculated according to the time series to obtain the normal vector of the plane where the track of the spatial target corresponding to the radar detection data segment is located.

[0017] Optionally, the following steps are used to obtain the quadratic fitting function of the track in advance: Obtain the radar detection data with clear tracks, and evenly divide the radar detection data with clear tracks into several segments according to the time length to obtain several segments of radar detection data fragments with clear tracks.

[0018] For any segment of radar detection data fragment with a clear track, determine the three-dimensional space coordinates of the corresponding space target in the inertial coordinate system according to the radar installation position and the detection data in the radar detection data fragment.

[0019] According to the time median of the radar detection data fragment and the three-dimensional space coordinates of the corresponding space target in the inertial coordinate system, fit the quadratic fitting function of the track in the least squares method.

[0020] Optionally, the quadratic fitting function of the track is shown as follows: .

[0021] Where x , y , z are the three-dimensional space coordinates 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 respectively, t is the time point value of the radar detection data.

[0022] Optionally, based on the motion characteristic parameters of the space target corresponding to each segment of radar detection data fragment, the tracks of each space target are associated and fused according to the appearance time of each space target to obtain the tracks of several space targets, which specifically include the following steps: According to the time interval between the appearances of the space targets corresponding to any two segments of radar detection data fragments and the time interval threshold range, determine whether the space targets corresponding to the two segments of radar detection data fragments are likely to be the same space target.

[0023] If the time interval between the occurrences of the space targets corresponding to 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.

[0024] If the time interval between the occurrences of the space targets corresponding to 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.

[0025] If the time interval between the occurrences of the space targets corresponding to two radar detection data segments is within the time interval threshold range, the trajectories of the two space targets are extrapolated to the same moment, and when the comparison result of the motion characteristic parameters is less than the comparison error threshold, the trajectories of the two space targets are associated and fused; repeat the above steps to obtain the trajectories of several space targets.

[0026] Optionally, perform motion characteristic analysis based on the trajectories of space targets, identify the target types of space targets and output them, specifically including the following steps: Based on the altitude and severe acceleration of the space target, preliminarily determine whether the space target is a near-space target.

[0027] Based on the speed and acceleration of the space target, further determine whether the space target is a powered space target and the magnitude of its motion ability.

[0028] Based on the normal vector of the plane where the trajectory of the space target is located, determine whether the space target is a randomly moving space target.

[0029] In a second aspect, the present application provides a target type recognition system based on radar detection data, including the following functional modules: A radar detection data acquisition and segmentation module, configured to acquire initial radar detection data to be recognized, and evenly divide the initial radar detection data into several segments according to the time length to obtain several radar detection data segments.

[0030] A space target motion characteristic parameter solving module, configured to, for any radar detection data segment, determine the motion characteristic parameters of the space target corresponding to the radar detection data segment according to a pre-fitted trajectory quadratic fitting function; the trajectory quadratic fitting function is used to characterize the non-linear 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 speed, acceleration, altitude, gravitational acceleration, and the normal vector of the plane where the trajectory is located.

[0031] A spatial target track judgment and association fusion module, which is used to perform association fusion on the tracks of each spatial target according to the motion characteristic parameters of the spatial target corresponding to each segment of radar detection data fragments, and obtain the tracks of several spatial targets according to the appearance time of each spatial target.

[0032] A spatial target motion characteristic analysis and recognition module, which is used to perform motion characteristic analysis on the track of any spatial target, identify the target type of the spatial target and output it according to the track of the spatial target; the target type includes whether it is a near-space spatial target, whether it has power, whether it is a randomly moving spatial target, and the magnitude of the motion ability of the spatial target.

[0033] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the target type recognition method based on radar detection data described above.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the target type recognition method based on radar detection data described above.

[0035] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application: The present application provides a target type recognition method, system, device and medium based on radar detection data. The method includes: obtaining initial radar detection data to be recognized and uniformly dividing the initial radar detection data into several segments according to the time length, and determining the motion characteristic parameters of the spatial target corresponding to each radar detection data fragment according to the pre-fitted track quadratic fitting function; then performing association fusion on the tracks of each spatial target according to the appearance time of each spatial target, and finally performing motion characteristic analysis on the tracks of each spatial target, identifying the target type of the spatial target and outputting it. By segmenting the detection data, using the pre-fitted track quadratic fitting function for track extrapolation comparison and integrating the tracks of multiple spatial targets, and then identifying the spatial target type through the motion characteristic analysis of the tracks, the present application can assist in judging the correctness of radar tracking and provide an auxiliary decision-making role for correctly executing the tracking plan. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 This is an application environment diagram of a method for identifying target types based on radar detection data provided by an embodiment of the present application.

[0038] Figure 2 This is a flowchart of a method for identifying target types based on radar detection data provided by an embodiment of the present application.

[0039] Figure 3 This is a flowchart of step A2 in a method for identifying target types based on radar detection data provided by an embodiment of the present application.

[0040] Figure 4 This is a flowchart of step A23 in a method for identifying target types based on radar detection data provided by an embodiment of the present application.

[0041] Figure 5 This is a flowchart of step A3 in a method for identifying target types based on radar detection data provided by an embodiment of the present application.

[0042] Figure 6 This is a flowchart of step A4 in a method for identifying target types based on radar detection data provided by an embodiment of the present application.

[0043] Figure 7 This is a flowchart of obtaining a quadratic fitting function of a track by pre-fitting in a method for identifying target types based on radar detection data provided by an embodiment of the present application.

[0044] Figure 8 This is a schematic diagram of functional modules of a system for identifying target types based on radar detection data provided by an embodiment of the present application.

[0045] Figure 9 This is a schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0046] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0047] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0048] The target type recognition method based on radar detection data provided by the embodiments of the present application can be applied to, for example, Figure 1 the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers.

[0049] The terminal 102 can send the initial radar detection data to be recognized to the server 104. After receiving the initial radar detection data to be recognized, the server 104 evenly divides the initial radar detection data into several segments according to the time length to obtain several radar detection data segments. For any segment of radar detection data segment, according to the pre-fitted track quadratic fitting function, determine the motion characteristic parameters of the spatial target corresponding to the radar detection data segment; the track quadratic fitting function is used to characterize the non-linear relationship between the detection time of the radar detection data and the coordinates of the spatial target in the inertial coordinate system; the motion characteristic parameters include speed, acceleration, height, gravitational acceleration, and the normal vector of the plane where the track is located. Based on the motion characteristic parameters of the spatial targets corresponding to each segment of radar detection data segment, the tracks of each spatial target are associated and fused according to the appearance time of each spatial target to obtain the tracks of several spatial targets. For the track of any spatial target, perform motion characteristic analysis according to the track of the spatial target, identify the target type of the spatial target and output it. The server 104 can feedback the obtained spatial target number and target type to the terminal 102.

[0050] In addition, in some embodiments, the target type recognition method based on radar detection data can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly process the initial radar detection data to be recognized, or the server 104 can obtain the initial radar detection data to be recognized from the data storage system and process it. Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0051] In an exemplary embodiment, as Figure 2 shown, a target type recognition method based on radar detection data is provided. This method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, this method is applied toFigure 1 Taking the server 104 in [reference] as an example, the method includes the following steps: A1. Obtain the initial radar detection data to be recognized, and evenly divide the initial radar detection data 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, the target operation state, and its own errors during detection, there are certain accidental errors in the data at a single point. Therefore, the method of taking values by sliding window segmentation is adopted, combined with the time median of the subsequent segments t as the independent variable of the fitting function, and the spatial coordinates can be solved and the motion characteristics can be calculated to eliminate the influence of the above accidental errors.

[0052] A2. For any radar detection data segment, determine the motion characteristic parameters of the spatial target corresponding to the radar detection data segment according to the pre-fitted track quadratic fitting function; the track quadratic fitting function is used to characterize the non-linear relationship between the detection time of the radar detection data and the coordinates of the spatial target in the inertial coordinate system; the motion characteristic parameters include speed, acceleration, height, gravitational acceleration, and the normal vector of the plane where the track is located. Specifically, as Figure 3 shown, step A2 includes the following steps: A21. Take the time median of the radar detection data segment, and determine the three-dimensional spatial coordinates of the spatial target corresponding to the radar detection data segment in the inertial coordinate system according to the pre-fitted track quadratic fitting function. Through the installation position of the radar, the azimuth angle, pitch angle, detection distance, and detection time in the detection data, the three-dimensional coordinates of the spatial target in the earth-fixed coordinate system can be calculated. However, due to the influence of the earth's rotation, the continuous coordinate sequence in the earth-fixed coordinate system is not necessarily in the same plane. Therefore, it is converted to the inertial coordinate, and the J2000 inertial coordinate system is adopted in this embodiment.

[0053] Specifically, since the initial radar detection data is segmented by using the sliding window method in step A1, only one time point value is taken as the input for each segment. Here, the time point value in the middle of the segment is taken for calculation to reduce the influence of accidental errors.

[0054] In this embodiment, the track quadratic fitting function is shown as the following formula: .

[0055] Wherein, x , y , z are the three-dimensional spatial coordinates of the spatial target in the inertial coordinate system, a x 、 a y 、 a z 、 bx , b y , b z , c x , c y , c z are respectively the fitting coefficients, t and are the time point values of the radar detection data.

[0056] A22. Calculate the first derivative and the second derivative of the three-dimensional space coordinates of the spatial target corresponding to the radar detection data segment respectively, and determine the velocity, acceleration, altitude, and gravitational acceleration of the spatial target corresponding to the radar detection data segment in combination with the law of universal gravitation. Use the solved x , y , z , values and the universal gravitation to solve the motion characteristic parameters of the spatial target at any moment.

[0057] A23. Perform three-dimensional space coordinate calculations for several time points for the radar detection data segment, and determine the normal vector of the plane where the track of the spatial target corresponding to the radar detection data segment is located according to the cross product between the three-dimensional space coordinate vectors of every two adjacent time points. Specifically, as Figure 4 shown, step A23 includes the following steps: A231. For the radar detection data segment, take several time points at equal intervals within the time range of the radar detection data segment to obtain a time series. At the start time t 1 and the end time t n of the radar detection data segment, interpolate to obtain an equally spaced time series.

[0058] A232. For any time point, determine the three-dimensional space coordinates of the spatial target corresponding to the time point in the inertial coordinate system according to the pre-fitted track quadratic fitting function. Substitute the time points in the time series into the fitting function in sequence to solve x , y , z , and form a coordinate vector sequence.

[0059] A233. For any two adjacent time points in the time series, calculate the cross product of the two time points according to the three-dimensional space coordinates of the spatial target corresponding to the two time points in the inertial coordinate system. Take two adjacent coordinate vectors in the coordinate sequence in sequence, calculate their cross product, and obtain a cross product sequence.

[0060] A234. Calculate the average value of all cross products obtained according to the time series to obtain the normal vector of the plane where the track of the space target corresponding to the radar detection data segment is located. Take the average value of all cross product values in the cross product sequence as the normal vector of the plane where the track of the space target corresponding to the radar detection data segment is located.

[0061] A3. Based on the motion characteristic parameters of the space targets corresponding to each radar detection data segment, associate and fuse the tracks of each space target according to the appearance time of each space target to obtain the tracks of several space targets. In this embodiment, as Figure 5 shown, step A3 specifically includes the following steps: A31. According to the time interval between the appearances of the space targets corresponding to any two radar detection data segments and the time interval threshold range, determine whether the space targets corresponding to the two radar detection data segments may be the same space target.

[0062] 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, or 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, that is, the time interval between the appearances of the space targets corresponding to the two radar detection data segments is not within the time interval threshold range, then execute step A32. 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 range, then execute step A33.

[0063] A32. Determine that the space targets corresponding to the two radar detection data segments are not the same space target.

[0064] A33. Deduce the tracks of the two space targets to the same moment, and when the comparison result of the motion characteristic parameters is less than the comparison error threshold, associate and fuse the tracks of the two space targets.

[0065] A34. Repeat the judgment in step A31 above until the matching judgment of all radar detection data segments is completed to obtain the tracks of several space targets.

[0066] A4. For the track of any space target, perform motion characteristic analysis according to 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 near-space space target, whether it has power, whether it is a randomly moving space target, and the magnitude of the motion ability of the space target.

[0067] Specifically, as Figure 6 shown, step A4 includes the following steps: A41. Initially judge whether the space target is a near-space space target through the height and severe acceleration of the space target.

[0068] A42. Further determine whether the space target is a powered space target and the magnitude of its motion ability based on the speed and acceleration of the space target.

[0069] A43. Determine whether the space target is a randomly moving space target based on the normal vector of the plane where the track of the space target is located.

[0070] In an exemplary embodiment of the present application, it further includes a process of pre-fitting to obtain a quadratic fitting function of the track, as Figure 7 shown. This process includes the following steps: B1. Obtain the radar detection data with clear tracks, and evenly divide the radar detection data with clear tracks into several segments according to the time length to obtain several segments of radar detection data fragments with clear tracks.

[0071] B2. For any segment of radar detection data fragment with clear tracks, determine the three-dimensional space coordinates of the corresponding space target in the inertial coordinate system according to the radar installation position and the detection data in the radar detection data fragment.

[0072] B3. According to the time median of the radar detection data fragment and the three-dimensional space coordinates of the corresponding space target in the inertial coordinate system, fit to obtain a quadratic fitting function of the track in a least-squares manner.

[0073] The above-mentioned target type recognition method based on radar detection data proposed in the present application, through segmenting the detection data, using least-squares segment fitting to calculate the track motion characteristics, extrapolating and comparing through the fitting function to integrate multi-target tracks, and then performing target type recognition through the motion characteristics of the track, can assist in judging the correctness of radar tracking and provide an auxiliary decision-making role for correctly executing the tracking plan.

[0074] Based on the same inventive concept, the embodiments of the present application also provide a system for implementing the above-mentioned target type recognition method based on radar detection data. The implementation solutions provided by this system to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more system embodiments provided below can refer to the limitations on the target type recognition method based on radar detection data in the above text, and will not be repeated here.

[0075] In an exemplary embodiment, as Figure 8 shown, a target type recognition system based on radar detection data is provided, including the following functional modules: Radar detection data acquisition and segmentation module, which is used to acquire the initial radar detection data to be recognized and evenly divide the initial radar detection data into several segments according to the time length to obtain several segments of radar detection data fragments.

[0076] 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 according to the pre-fitted track quadratic fitting function; the track quadratic fitting function is used to characterize the non-linear 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 speed, acceleration, altitude, gravitational acceleration, and the normal vector of the plane where the track is located.

[0077] The space target track judgment and association fusion module is used to perform association fusion on the tracks of each space target based on the motion characteristic parameters of the space target corresponding to each radar detection data segment, and obtain the tracks of several space targets according to the appearance time of each space target.

[0078] The space target motion characteristic analysis and recognition module is used to perform motion characteristic analysis on the track of any space target, identify the target type of the space target and output it; the target type includes whether it is a near-space space target, whether it has power, whether it is a randomly moving space target, and the magnitude of the motion ability of the space target.

[0079] Of course, Figure 8 The shown architecture is only exemplary. When implementing different functions, one or at least two components in the shown system can be omitted according to actual needs. Figure 8 shown in the system.

[0080] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the 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 external devices. The communication interface of the computer device is used to communicate with external terminals 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 foregoing embodiments can be implemented.

[0081] Those skilled in the art can understand, Figure 9The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0082] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0083] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0084] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0085] 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 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 need to comply with relevant regulations.

[0086] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0087] The databases involved in the various embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0088] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0089] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for identifying target types based on radar detection data, characterized in that Including: Obtain the initial radar detection data to be recognized, and evenly divide the initial radar detection data into several segments according to the time length to obtain several radar detection data segments; For any radar detection data segment, determine the motion characteristic parameters of the space target corresponding to the radar detection data segment according to the pre-fitted track quadratic fitting function; the track quadratic fitting function is used to characterize the non-linear 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 speed, acceleration, altitude, gravitational acceleration, and the normal vector of the plane where the track is located; Based on the motion characteristic parameters of the space targets corresponding to each radar detection data segment, associate and fuse the tracks of each space target according to the appearance time of each space target to obtain the tracks of several space targets; For the track of any space target, perform motion characteristic analysis according to 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 near-space space target, a space target with or without power, whether it is a randomly moving space target, and the magnitude of the motion ability of the space target.

2. The method for identifying target types based on radar detection data according to claim 1, wherein Determine the motion characteristic parameters of the space target corresponding to the radar detection data segment according to the pre-fitted track quadratic fitting function, specifically including: Take the time median of the radar detection data segment, and determine the three-dimensional space coordinates of the space target corresponding to the radar detection data segment in the inertial coordinate system according to the pre-fitted track quadratic fitting function; Calculate the first derivative and the second derivative of the three-dimensional space coordinates of the space target corresponding to the radar detection data segment respectively, and combine the law of universal gravitation to determine the speed, acceleration, altitude, and gravitational acceleration of the space target corresponding to the radar detection data segment; Perform three-dimensional space coordinate calculations for several time points of the radar detection data segment, and determine the normal vector of the plane where the track of the space target corresponding to the radar detection data segment is located according to the cross product between the three-dimensional space coordinate vectors of every two adjacent time points; 3. The method for identifying target types based on radar detection data according to claim 2, characterized in that, Perform three-dimensional space coordinate calculations for several time points of the radar detection data segment, and determine the normal vector of the plane where the track of the space target corresponding to the radar detection data segment is located according to the cross product between the three-dimensional space coordinate vectors of every two adjacent time points, specifically including: For the radar detection data segment, take several time points at equal intervals within the time range of the radar detection data segment to obtain a time series; For any time point, determine the three-dimensional space coordinates of the space target corresponding to the time point in the inertial coordinate system according to the pre-fitted track quadratic fitting function; For any two adjacent time points in the time series, calculate the cross product of the two time points according to the three-dimensional space coordinates of the space target corresponding to the two time points in the inertial coordinate system; Average all the cross products calculated according to the time series to obtain the normal vector of the plane where the track of the space target corresponding to the radar detection data segment is located.

4. The method for identifying target types based on radar detection data according to claim 1, characterized in that, Obtain the track quadratic fitting function 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 time length to obtain several segments of radar detection data segments with clear tracks For any segment of radar detection data segment with clear tracks, determine the three-dimensional space coordinates of the corresponding space target in the inertial coordinate system according to 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, fit to obtain a track quadratic fitting function in the least squares manner.

5. The method for identifying target types based on radar detection data according to claim 1, wherein The track quadratic fitting function is shown as the following formula: ； Among them, x , y , z are the three-dimensional space coordinates 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 respectively, t is the time point value of the radar detection data.

6. The method for identifying target types based on radar detection data according to claim 1, wherein Based on the motion characteristic parameters of the corresponding space targets of each segment of radar detection data segments, associate and fuse the tracks of each space target according to the appearance time of each space target to obtain the tracks of several space targets, specifically including: According to the time interval between the appearances of the space targets corresponding to any two segments of radar detection data segments and the time interval threshold range, determine whether the space targets corresponding to the two segments of radar detection data segments are likely to be the same space target; If the time interval between the appearances of the space targets corresponding to the two segments of 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 segments of 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 segments of 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 segments of 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 segments of radar detection data segments is within the time interval threshold range, project the tracks of the two space targets to the same moment, and when the comparison result of the motion characteristic parameters is less than the comparison error threshold, associate and fuse the tracks of the two space targets; repeat the above steps to obtain the tracks of several space targets.

7. The method for identifying target types based on radar detection data according to claim 1, wherein Conduct motion characteristic analysis according to the tracks of the space targets, identify the target types of the space targets and output them, specifically including: Preliminarily judge whether the space target is a near-space target through the height and severe acceleration of the space target; Further judge whether the space target is a powered space target and the magnitude of its motion ability through the speed and acceleration of the space target; Judge whether the space target is a randomly moving space target through 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, Including: A radar detection data acquisition and segmentation module, which 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 segments of radar detection data segments; A spatial target motion characteristic parameter solving module, which is used to determine the motion characteristic parameters of the spatial target corresponding to any radar detection data segment according to the pre-fitted track quadratic fitting function; the track quadratic fitting function is used to characterize the non-linear relationship between the detection time of the radar detection data and the coordinates of the spatial target in the inertial coordinate system; the motion characteristic parameters include speed, acceleration, altitude, gravitational acceleration, and the normal vector of the plane where the track is located; A spatial target track judgment and correlation fusion module, which is used to perform correlation fusion on the tracks of each spatial target according to the motion characteristic parameters of the spatial target corresponding to each radar detection data segment, and obtain the tracks of several spatial targets according to the appearance time of each spatial target; A spatial target motion characteristic analysis and recognition module, which is used to perform motion characteristic analysis on the track of any spatial target, identify the target type of the spatial target and output it; the target type includes whether it is a near-space spatial target, whether it has power, whether it is a randomly moving spatial target, and the magnitude of the motion ability of the spatial target.

9. A computer device, comprising: A memory, a processor, and a computer program stored on 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 1-7.

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

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