Dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method and system based on digital array radar

Abstract

The application discloses a dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method and system based on a digital array radar, and particularly relates to the following steps: setting radar system parameters, establishing a time-varying motion model of the unmanned aerial vehicle cluster, and obtaining formation pose distribution of the unmanned aerial vehicle cluster in a global coordinate system; performing component decomposition on a CAD model of the unmanned aerial vehicle, performing occlusion analysis under the constraint condition of a radar line of sight, extracting time-varying scattering center parameters according to dynamic electromagnetic effects between targets, updating the spatial distribution of the scattering centers of the unmanned aerial vehicle cluster in a time sequence recursion mode, and completing the construction of a time-varying scattering center model; constructing a radar system signal model according to digital array radar (DAR) parameters, performing multi-dimensional dynamic characteristic extraction and parameter mapping, completing the construction of multi-channel signal modulation of the digital array radar and radar echo of the unmanned aerial vehicle cluster target, and realizing radar scattering electromagnetic characteristic modeling of the dynamic unmanned aerial vehicle cluster. The application improves the model description accuracy and is more efficient in the calculation process.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electromagnetic calculation and radar perception, and particularly relates to a dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method and system based on a digital array radar. BACKGROUND

[0002] In modern radar detection and identification systems, dynamic cluster targets cause inherent complex motion patterns, highly dynamic spatial structures, and non-negligible electromagnetic coupling effects between targets, and the overall radar scattering characteristics of the targets present high complexity and time variability. In-depth understanding and accurate characterization of the electromagnetic response behavior of such targets are not only the deepening demand of electromagnetic scattering theory itself, but also the theoretical basis domain model support for realizing effective sensing, accurate tracking, behavior understanding, and countermeasure strategies of cluster targets. In view of the heterogenization and high dynamic characteristics of unmanned aerial vehicle clusters in complex environments, current research on unmanned aerial vehicle cluster radar characteristics faces dual restrictions in data acquisition and modeling: on the one hand, due to the high experimental cost and entity scale constraints, it is difficult to build a real scene of large-scale unmanned aerial vehicle cluster and radar confrontation, and it is impossible to obtain radar echo data covering specific types and quantities; on the other hand, the traditional radar echo model has obvious deficiencies in the description accuracy of key dimensions such as formation coordination change and electromagnetic feature evolution. Therefore, it is an urgent need to construct a high-precision dynamic unmanned aerial vehicle cluster radar characteristic model to support the effectiveness evaluation of radar systems.

[0003] Traditional scattering center models are mainly constructed for static or simple motion targets, and face new challenges in describing the dynamic electromagnetic characteristics of cluster targets. Cluster formation motion not only affects the spatiotemporal distribution characteristics of scattering centers, but also brings about the group coordination evolution of scattering characteristics due to the motion constraints and electromagnetic coupling effects between individuals. In the background of the rapid development of typical application scenarios such as unmanned aerial vehicle clusters, it is urgent to explore the internal relationship between cluster coordinated motion law and electromagnetic scattering characteristics, and to construct a new electromagnetic scattering characterization model that integrates dynamic characteristics.

[0004] In the theory of radar echo modeling, existing researches usually assume based on statistical fluctuation models, and construct echo models driven by the statistical characteristics of isolated targets or idealized point scatterers. Such methods can realize echo simulation of single targets with low computational cost by simplifying the spatial correlation of target structure and scattering center. However, with the rise of group target application scenarios such as unmanned aerial vehicle clusters, the dynamic occlusion effect caused by coordinated motion and the complex interaction behavior of coupling scattering between targets lead to significant deviation between the prediction results of traditional models and the actual radar echoes. In view of this problem, researchers have made some progress in radar echo modeling combined with target characteristics, but it is still necessary to further explore the high-precision echo model that is suitable for the dynamic interaction of group targets and is based on digital array radar. SUMMARY

[0005] The application aims to provide a dynamic unmanned aerial vehicle cluster electromagnetic characteristic high-fidelity modeling method and system based on digital array radar, which has high model description accuracy and high calculation process efficiency.

[0006] The technical solution for achieving the application is as follows: a dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method based on digital array radar, comprising the following steps:

[0007] Step 1: setting radar system parameters by analyzing unmanned aerial vehicle cluster target performance parameters, establishing a time-varying motion model of the unmanned aerial vehicle cluster, and obtaining the formation pose distribution of the unmanned aerial vehicle cluster in a global coordinate system;

[0008] Step 2: taking the formation pose distribution as input, performing component decomposition on the unmanned aerial vehicle CAD model, performing occlusion analysis under the radar line-of-sight constraint condition, extracting time-varying scattering center parameters according to the dynamic electromagnetic effect between targets, and updating the spatial distribution of the scattering center of the unmanned aerial vehicle cluster in a time sequence recursion manner to complete the construction of the time-varying scattering center model;

[0009] Step 3: constructing a radar system signal model according to the digital array radar (DAR) parameters, performing multi-dimensional dynamic feature extraction and parameter mapping based on the time-varying scattering center model, completing the construction of the digital array radar multi-channel signal modulation and the unmanned aerial vehicle cluster target radar echo, and realizing the radar scattering electromagnetic characteristic modeling of the dynamic unmanned aerial vehicle cluster.

[0010] A dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling system based on digital array radar, which is used for realizing the dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method based on digital array radar, and comprises a time-varying motion model construction module, a time-varying scattering center model construction module and a radar system signal model construction module, and the functions of the modules are as follows:

[0011] The time-varying motion model construction module sets radar system parameters by analyzing unmanned aerial vehicle cluster target performance parameters, establishes a time-varying motion model of the unmanned aerial vehicle cluster, and obtains the formation pose distribution of the unmanned aerial vehicle cluster in a global coordinate system;

[0012] The time-varying scattering center model construction module takes the formation pose distribution as input, performs component decomposition on the unmanned aerial vehicle CAD model, performs occlusion analysis under the radar line-of-sight constraint condition, extracts time-varying scattering center parameters according to the dynamic electromagnetic effect between targets, and updates the spatial distribution of the scattering center of the unmanned aerial vehicle cluster in a time sequence recursion manner to complete the construction of the time-varying scattering center model;

[0013] A radar system signal model construction module constructs a radar system signal model according to digital array radar (DAR) parameters, performs multi-dimensional dynamic feature extraction and parameter mapping based on a time-varying scattering center model, completes digital array radar multi-channel signal modulation and unmanned aerial vehicle cluster target radar echo construction, and realizes radar scattering electromagnetic characteristic modeling of a dynamic unmanned aerial vehicle cluster.

[0014] A mobile terminal comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the digital array radar-based dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method when executing the program.

[0015] A computer readable storage medium has a computer program stored thereon, and the program is executed by a processor to implement the steps in the digital array radar-based dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method.

[0016] Compared with the prior art, the present application has the following advantages: (1) considering the dynamic constraints of the target, the dynamic reconstruction of the unmanned aerial vehicle cluster formation configuration and the scattering center model is realized, and the description of the motion characteristics is more realistic; (3) through deep mapping analysis of the multi-channel modulation characteristics of the digital array radar (DAR) and the scattering mechanism of the target cluster, the parameterized mapping relationship between the target characteristics and the radar response is constructed, and the precise modeling of the unmanned aerial vehicle cluster target radar echo is realized in combination with the target electromagnetic scattering characteristics, the radar modulation and the noise model; (3) the multi-dimensional electromagnetic scattering characteristics of the cluster target radar characteristics are calculated by using the dynamic scattering center model, the calculation process is more efficient, and accurate theoretical and technical support is provided for the perception of the cluster target and the radar countermeasure effectiveness evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of the digital array radar-based dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method of the present application.

[0018] Figure 2 is a CAD model and simulation parameters of an unmanned aerial vehicle target in an embodiment of the present application.

[0019] Figure 3 is a cooperative flight control model of an unmanned aerial vehicle cluster in an embodiment of the present application.

[0020] Figure 4 is a configuration distribution model of a heterogeneous unmanned aerial vehicle cluster in an embodiment of the present application.

[0021] Figure 5 is dynamic RCS data of an unmanned aerial vehicle cluster in an embodiment of the present application.

[0022] Figure 6 is HRRP characteristics of an unmanned aerial vehicle cluster at one observation time in an embodiment of the present application.

[0023] Figure 7 is the time-varying azimuth angle flicker error and the modified radar azimuth angle of the UAV cluster in the embodiment of the application.

[0024] Figure 8 is the time-varying pitch angle flicker error and the modified radar pitch angle of the UAV cluster in the embodiment of the application.

[0025] Figure 9 is the individual dynamic RCS and the equivalent position of the UAV cluster in the embodiment of the application.

[0026] Figure 10 is the equivalent point target pulse compression result and the time-varying echo energy of the UAV cluster in the embodiment of the application.

[0027] Figure 11 is the equivalent extended target pulse compression result and the antenna pattern of the UAV cluster in the embodiment of the application.

[0028] Figure 12 is the distributed individual modeling pulse compression result and the antenna pattern of the UAV cluster in the embodiment of the application. DETAILED DESCRIPTION

[0029] In order to make the above objectives, characteristics and advantages of the application more apparent, obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the application.

[0030] The application provides a dynamic UAV cluster electromagnetic characteristic modeling method based on a digital array radar, comprising the following steps:

[0031] Step 1, by analyzing the target performance parameters of the UAV cluster, setting the radar system parameters, establishing the time-varying motion model of the UAV cluster, and obtaining the formation pose distribution of the UAV cluster in the global coordinate system;

[0032] Step 2, taking the formation pose distribution as the input, performing component decomposition on the UAV CAD model, performing occlusion analysis under the radar line-of-sight constraint condition, extracting the time-varying scattering center parameters according to the dynamic electromagnetic effect between targets, and updating the spatial distribution of the scattering center of the UAV cluster in a time sequence recursion manner, to complete the construction of the time-varying scattering center model;

[0033] Step 3, constructing a radar system signal model according to a digital array radar (DAR) parameter, performing multi-dimensional dynamic feature extraction and parameter mapping based on a time-varying scattering center model, completing digital array radar multi-channel signal modulation and unmanned aerial vehicle (UAV) cluster target radar echo construction, and realizing radar scattering electromagnetic characteristic modeling of a dynamic UAV cluster.

[0034] As a specific example, in step 1, the performance parameters of the UAV cluster target are analyzed, the radar system parameters are set, the time-varying motion model of the UAV cluster is established, and the formation pose distribution of the UAV cluster in the global coordinate system is obtained, which is specifically as follows:

[0035] Step 1.1, analyzing the performance parameters of the UAV cluster target, including formation configuration, motion performance, task indicators and fault tolerance requirements, to obtain a set of quantitative performance indicators;

[0036] Step 1.2, setting radar system parameters, constructing a virtual host route, and establishing a time-varying motion model of the UAV cluster;

[0037] Step 1.3, executing a formation following cooperative control strategy to generate distributed obstacle avoidance and pose correction control instructions;

[0038] Step 1.4, based on the control instructions, solving the dynamic parameters through the time-varying motion model to obtain the formation pose distribution of the UAV cluster in the global coordinate system, including the position of the virtual host and the real-time position and attitude parameters of the followers.

[0039] As a specific example, in step 2, the formation pose distribution is taken as input, the UAV CAD model is decomposed into components, the occlusion analysis is performed under the radar line-of-sight constraint condition, the time-varying scattering center parameters are extracted according to the dynamic electromagnetic effect between targets, and the scattering center spatial distribution of the UAV cluster is updated in a time sequence recursion manner to complete the construction of the time-varying scattering center model, which is specifically as follows:

[0040] Step 2.1, based on the radar parameters and the formation pose distribution, the position of the virtual host in the global coordinate system and the real-time position and attitude parameters of the followers are mapped to the local coordinate system through parameter solving conversion to obtain the real-time formation pose parameters in the local coordinate system;

[0041] Step 2.2, taking the UAV CAD model, the radar parameters and the real-time formation pose parameters in the local coordinate system as electromagnetic calculation input;

[0042] Step 2.3, decomposing the UAV CAD model into components and performing occlusion analysis under the radar line-of-sight constraint condition;

[0043] Step 2.4, extracting time-varying scattering center parameters according to the dynamic electromagnetic effect between targets, the time-varying scattering center parameters is represented as , including the three-dimensional position of the th scattering center and the amplitude of the th scattering center ;

[0044] Step 2.5, using a time sequence recursion calculation method, the spatial distribution of scattering centers of the UAV cluster is updated at the radar observation time step, the construction of the time-varying scattering center model is completed, the cooperative reconstruction of the formation configuration and the time-varying scattering center model is realized, and the expression of the time-varying scattering center model at a certain radar observation time is as follows:

[0045]

[0046]

[0047] Wherein, CPI represents the coherent processing interval, represents the index of CPI and , represents the pulse index inside CPI and , represents the number of observation frames, represents the number of pulses per frame; is the pulse repetition interval;

[0048] represents the current processing time, which is calculated from the CPI index and the pulse index; represents the time point of the current observation time, which is determined by the th CPI and the th pulse index in the CPI; represents the observation starting time, that is, the starting time of the entire radar observation process; represents the time length of the coherent processing interval; represents the carrier frequency at time , the time-varying scattering center model observed by the radar from direction ;

[0049] is the number of scattering centers, is the amplitude of the th scattering center, is the three-dimensional position of the th scattering center, is the free space electromagnetic wave propagation speed, represents the imaginary unit, is the carrier frequency, is the unit vector of the radar line of sight direction, and are azimuth and elevation angles of the radar line of sight, respectively.

[0050] As a specific example, step 3 describes constructing a radar system signal model according to the digital array radar (DAR) parameters, performing multi-dimensional dynamic feature extraction and parameter mapping based on a time-varying scattering center model, completing digital array radar multi-channel signal modulation and unmanned aerial vehicle (UAV) cluster target radar echo construction, and realizing dynamic UAV cluster radar scattering electromagnetic characteristic modeling, as follows:

[0051] Step 3.1, constructing a radar system signal model according to the digital array radar (DAR) parameters;

[0052] Step 3.2, performing multi-dimensional dynamic feature extraction based on a time-varying scattering center model, including dynamic RCS, HRRP, and angle glint parameters, where RCS represents radar scattering cross section, and HRRP represents high-resolution range profile;

[0053] Step 3.3, under the digital array radar framework, constructing a parameterized mapping relationship between multi-dimensional dynamic features and digital array radar multi-channel signal modulation, and outputting target radar echo data meeting the needs of group detection, group tracking, and individual perception;

[0054] Step 3.4, combining target electromagnetic scattering characteristics, signal modulation, and noise models, realizing dynamic UAV cluster radar scattering electromagnetic characteristic modeling.

[0055] As a specific example, step 3.2 describes performing multi-dimensional dynamic feature extraction based on a time-varying scattering center model, as follows:

[0056] Step 3.2.1, combining formation pose distribution and time-varying scattering center model to calculate RCS at each observation time, iterating all observation time sequences to obtain dynamic RCS of the target group, expressed as:

[0057]

[0058] where, represents RCS of the UAV cluster at time

[0059] Step 3.2.2, for HRRP generation of the dynamic cluster target, based on the real-time nature of the cluster configuration time-varying characteristics and radar observation geometry, the scattering centers of the cluster target are divided according to distance units, and each distance unit echo is generated in real time and sequentially superimposed to form HRRP, thereby simulating the distance expansion characteristics of the cluster target in the radar line of sight, providing a feature basis for cluster detection and distance resolution, expressed as:

[0060] ​

[0061] where, denotes the time echo in the th range cell, denotes the time time-varying scattering center model in the th range cell; is the number of scattering centers in the th range cell, is the amplitude of the th scattering center in the th range cell, is the three-dimensional position vector of the th scattering center in the th range cell, and the target one-dimensional range image is:

[0062]

[0063] where, denotes the one-dimensional range image of the target at time is the modulus of the echo in the th range cell at time denotes the range cell number, is the total number of range cells;

[0064] Step 3.2.3. In the observation of a cluster target by a radar, angular glint is used to simulate the observation deviation of the cluster target in the angular dimension of the radar. A relationship between the phase gradient and the angular correction is established based on the target geometric model to quantify the observation deviation.

[0065] The total echo is the vector sum of the echoes of each scattering center, and the complex signal form is:

[0066]

[0067] where, denotes the total echo of all scattering centers of the target, is the instantaneous phase of the th scattering center, is the total echo amplitude, is the total phase;

[0068] The phase gradient is converted into a linear deviation through the wave number The offset amount of the radar line-of-sight angle in the direction of the geometric center of the target is as follows:

[0069]

[0070] where,​​ This is the azimuth deviation. For pitch angle deviation, It is the azimuth angle. The pitch angle; , Substitute the target distances respectively Obtain the angle correction for azimuth and elevation angles. , The distance between the target and the radar; the updated radar line-of-sight angle, i.e., the azimuth and elevation angles corresponding to the radar's apparent center, are respectively... ;

[0071] Step 3.2.4: Based on dynamic scattering center clustering, obtain the RCS and equivalent position of individual targets within the UAV swarm, as detailed below:

[0072] The RCS of an individual goal is:

[0073]

[0074] Equivalent position:

[0075]

[0076] in, Indicates at time No. RCS of each target Indicates the first The complex number of echoes from a target, For the first The set of all scattering centers of a target For the first drone cluster The equivalent position of each target after occlusion is considered.

[0077] As a specific example, step 3.3 describes constructing a parameterized mapping relationship between multi-dimensional dynamic features and multi-channel signal modulation of the digital array radar within the digital array radar framework, and outputting target radar echo data that meets the requirements of group detection, group tracking, and individual perception, as follows:

[0078] Step 3.3.1: In the equivalent point target modeling, the entire swarm of UAVs flying in cooperative flight is simplified into a single scattering point, which is characterized by the overall radar cross section and the geometric center position of the swarm as follows:

[0079]

[0080] in, Indicates receiving signal, This represents the signal after the propagation delay caused by the target distance. This represents the current reception time in the radar system. To delay the spread, For a moment Doppler shift, For wavelength, For the total RCS of the drone swarm, The distance between the radar and the geometric center of the cluster. For the radar gain in the direction of the geometric center of the UAV swarm, The azimuth angle corresponding to the geometric center of the drone swarm. The pitch angle corresponding to the geometric center of the drone swarm;

[0081] Step 3.3.2: Employing an equivalent extended target modeling approach, a radar echo representation model for a cluster of targets spanning multiple continuous range resolution cells is constructed by fusing structural information from HRRP with the dynamic variation characteristics of angular scintillation. The formula is as follows:

[0082]

[0083] in, Indicates the distance between the radar and the geometric center of the drone swarm. Indicates time In the Echoes from a distance unit, For time-delay pulse functions, To delay the spread, This is the gain in the radar's apparent center direction. and These are the azimuth and elevation angles corresponding to the radar's apparent center, respectively.

[0084] The radar echo characterization model describes the formation's spatial expansion and the dynamic evolution of the radar's apparent center, which is the angle of the cluster target perceived by the radar system during observation.

[0085] Step 3.3.3: Based on the RCS and equivalent position of individual targets obtained from dynamic scattering center clustering, construct a distributed individual radar echo model:

[0086]

[0087] in, Indicates receiving signal, Indicates the target number within the drone swarm. For the target total number, For the first The signal after propagation delay caused by the distance to the target. For the first The propagation delay of each target For the first a target moment a Doppler shift, RCS of the first target, RCS of the first target, a distance of the first target from the radar, a distance of the first target from the radar, a gain of the radar in the direction of the first target, a gain of the radar in the direction of the first target, a gain of the radar in the direction of the first target, a gain of the radar in the direction of the first target, a gain of the radar in the direction of the first target.

[0088] As a specific example, the combination of target electromagnetic scattering characteristics, signal modulation and noise model described in step 3.4 realizes the modeling of the radar scattering electromagnetic characteristics of the dynamic UAV cluster, specifically as follows:

[0089] The radar echo signal is formed by the coupling of the multi-physical effects of target scattering intensity, propagation attenuation, time delay and noise, and the received signal model is expressed as:

[0090]

[0091] wherein, represents the received signal, is the signal after the propagation delay caused by the target distance is the Doppler shift, is the RCS of the dynamic UAV cluster, is the RCS of the dynamic UAV cluster, is the radar pattern gain, is the noise.

[0092] The application also provides a dynamic UAV cluster electromagnetic characteristic modeling system based on a digital array radar, which is used to realize the dynamic UAV cluster electromagnetic characteristic modeling method based on a digital array radar, and the system comprises a time-varying motion model construction module, a time-varying scattering center model construction module and a radar system signal model construction module, and the functions of the modules are as follows:

[0093] The time-varying motion model construction module sets the radar system parameters by analyzing the performance parameters of the UAV cluster target, establishes the time-varying motion model of the UAV cluster, and obtains the formation pose distribution of the UAV cluster in the global coordinate system;

[0094] The time-varying scattering center model construction module takes the formation pose distribution as input, decomposes the UAV CAD model into components, performs occlusion analysis under the radar line-of-sight constraint condition, extracts the time-varying scattering center parameters according to the dynamic electromagnetic effect between targets, and updates the scattering center spatial distribution of the UAV cluster in a time sequence recursion manner, and completes the construction of the time-varying scattering center model;

[0095] A radar system signal model construction module constructs a radar system signal model according to digital array radar (DAR) parameters, performs multi-dimensional dynamic feature extraction and parameter mapping based on a time-varying scattering center model, completes digital array radar multi-channel signal modulation and unmanned aerial vehicle cluster target radar echo construction, and realizes radar scattering electromagnetic characteristic modeling of a dynamic unmanned aerial vehicle cluster.

[0096] The application further provides a mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for modeling electromagnetic characteristics of a dynamic unmanned aerial vehicle cluster based on a digital array radar when executing the program.

[0097] The application further provides a computer readable storage medium having a computer program stored thereon, wherein the program implements the steps in the method for modeling electromagnetic characteristics of a dynamic unmanned aerial vehicle cluster based on a digital array radar when executed by a processor.

[0098] The application will be described in further detail below with reference to the drawings and specific embodiments.

[0099] Embodiment

[0100] In combination Figure 1 The embodiment provides a method for high-fidelity modeling electromagnetic characteristics of a dynamic unmanned aerial vehicle cluster based on a digital array radar, comprising the following steps:

[0101] Step 1: by analyzing unmanned aerial vehicle cluster target performance parameters, setting radar system parameters, constructing a virtual host route and executing a formation following cooperative control strategy, finally outputting a virtual host position in a global coordinate system, i.e. real-time pose parameters of a follower;

[0102] Step 2: taking formation pose distribution as input, performing component decomposition on a unmanned aerial vehicle CAD model, performing occlusion analysis under the condition of applying radar line-of-sight constraints, realizing time-varying scattering center parameter extraction according to dynamic electromagnetic effects between targets, and updating the spatial distribution of scattering centers of the unmanned aerial vehicle cluster in a time sequence recursion manner, to complete construction of a time-varying scattering center model;

[0103] Step 3: constructing a system signal model according to digital array radar (DAR) parameters, performing multi-dimensional dynamic feature extraction and parameter mapping based on a time-varying scattering center model, completing channel signal modulation and unmanned aerial vehicle cluster target radar echo construction, and realizing high-fidelity modeling of radar scattering characteristics of a dynamic unmanned aerial vehicle cluster target.

[0104] Figure 2 is a CAD model and simulation parameters of a unmanned aerial vehicle target, Figure 3 is a cooperative flight control model of a unmanned aerial vehicle cluster, Figure 4 is a configuration distribution model of a heterogeneous unmanned aerial vehicle cluster. Figure 5is the dynamic RCS data of the UAV cluster, Figure 6 is the HRRP feature of the UAV cluster at an observation moment. Figure 7 is the time-varying azimuth angle glint error and the modified radar azimuth angle of the UAV cluster, Figure 8 is the time-varying elevation angle glint error and the modified radar elevation angle of the UAV cluster, Figure 9 is the individual dynamic RCS and the equivalent position of the UAV cluster.

[0105] Figure 10 the pulse compression result and the peak energy thereof match the Figure 5 dynamic RCS data, indicating the accuracy of the equivalent point target radar echo modeling; Figure 11 the pulse compression result matches the Figure 6 HRRP of the Figure 11 through the radar beam pattern in an observation period, the beam pointing direction matches the Figure 7 , Figure 8 modified azimuth angle and elevation angle, indicating the accuracy of the equivalent extended target radar echo modeling; Figure 12 the pulse compression result is similar to the Figure 11 pulse compression result, Figure 12 through the radar beam pattern in an observation period, the beam pointing direction matches the Figure 11 beam pointing direction, and the match of the Figure 9 individual dynamic RCS and the equivalent position verifies the accuracy of the equivalent extended target and distributed individual modeling radar echo modeling.

[0106] The above is only the preferred embodiment of the present application, it should be noted that for those skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A dynamic unmanned aerial vehicle cluster electromagnetic property modeling method based on digital array radar, characterized in that, The method comprises the following steps: Step 1, by analyzing the performance parameters of the unmanned aerial vehicle cluster target, setting the radar system parameters, establishing the time-varying motion model of the unmanned aerial vehicle cluster, and obtaining the formation pose distribution of the unmanned aerial vehicle cluster in the global coordinate system; Step 2, taking the formation pose distribution as input, decomposing the unmanned aerial vehicle CAD model, performing occlusion analysis under the radar line-of-sight constraint condition, extracting the time-varying scattering center parameters according to the dynamic electromagnetic effect between targets, and updating the spatial distribution of the scattering center of the unmanned aerial vehicle cluster in a time sequence recursion manner to complete the construction of the time-varying scattering center model; Step 3, constructing the radar system signal model according to the digital array radar (DAR) parameters, performing multi-dimensional dynamic feature extraction and parameter mapping based on the time-varying scattering center model, completing the construction of the digital array radar multi-channel signal modulation and the radar echo of the unmanned aerial vehicle cluster target, and realizing the radar scattering electromagnetic characteristic modeling of the dynamic unmanned aerial vehicle cluster; In step 1, the performance parameters of the unmanned aerial vehicle cluster target are analyzed, the radar system parameters are set, the time-varying motion model of the unmanned aerial vehicle cluster is established, and the formation pose distribution of the unmanned aerial vehicle cluster in the global coordinate system is obtained, which is as follows: Step 1.1, analyze the performance parameters of the unmanned aerial vehicle cluster target, including formation configuration, motion performance, task index and fault tolerance requirement, to obtain a set of quantitative performance indexes; Step 1.2, set the radar system parameters, construct a virtual host route, and establish a time-varying motion model of the unmanned aerial vehicle cluster; Step 1.3, execute the formation following cooperative control strategy to generate distributed obstacle avoidance and pose correction control instructions; Step 1.4, based on the control instructions, the dynamic parameters are calculated through the time-varying motion model to obtain the formation pose distribution of the unmanned aerial vehicle cluster in the global coordinate system, including the position of the virtual host and the real-time position and attitude parameters of the followers; In step 2, the formation pose distribution is taken as input, the unmanned aerial vehicle CAD model is decomposed, the occlusion analysis is performed under the radar line-of-sight constraint condition, the time-varying scattering center parameters are extracted according to the dynamic electromagnetic effect between targets, and the spatial distribution of the scattering center of the unmanned aerial vehicle cluster is updated in a time sequence recursion manner to complete the construction of the time-varying scattering center model, which is as follows: Step 2.1, based on the radar parameters and the formation pose distribution, the position of the virtual host in the global coordinate system and the real-time position and attitude parameters of the followers are mapped to the local coordinate system through parameter calculation and conversion to obtain the real-time formation pose parameters in the local coordinate system; Step 2.2, taking the unmanned aerial vehicle CAD model, the radar parameters and the real-time formation pose parameters in the local coordinate system as electromagnetic calculation inputs; Step 2.3, decompose the unmanned aerial vehicle CAD model, and perform occlusion analysis under the radar line-of-sight constraint condition; Step 2.

4. Perform time-varying scatterer parameter extraction based on dynamic electromagnetic effects between targets, said time-varying scatterer parameter represents a three-dimensional position of the first scatterer and an amplitude of the first scatterer ; Step 2.5, the spatial distribution of the scattering center of the unmanned aerial vehicle cluster is updated in a time sequence recursion calculation manner at the radar observation time step to complete the construction of the time-varying scattering center model, realize the cooperative reconstruction of the formation configuration and the time-varying scattering center model, and the expression of the time-varying scattering center model at a certain radar observation time is as follows: ； ； wherein CPI denotes a coherent processing interval, denotes an index of CPI and , denotes a pulse index within CPI and , denotes a number of observation frames, denotes a number of pulses per frame; is a pulse repetition interval; denotes the current processing time, which is calculated from the CPI index and the pulse index; denotes the time point of the current observation time, which is determined by the th CPI and the th pulse index in the CPI; denotes the observation start time, which is the starting time of the whole radar observation process; denotes the time length of the coherent processing interval; denotes the carrier frequency at time , the time-varying scattering center model observed by the radar from direction at time is the number of scattering centers, is the amplitude of the th scattering center, is the three-dimensional position of the th scattering center, is the free-space electromagnetic wave propagation speed, denotes the imaginary unit, is the carrier frequency, is the unit vector of the radar line-of-sight direction, and are the azimuth and elevation angles of the radar line-of-sight, respectively; The step 3 is specifically as follows: Step 3.1, constructing a radar system signal model according to a digital array radar (DAR) parameter; Step 3.2, performing multi-dimensional dynamic characteristic adaptability extraction based on a time-varying scattering center model, including dynamic RCS, HRRP, and angle glint parameters, wherein RCS represents a radar scattering cross section, and HRRP represents a high-resolution range profile; Step 3.3, under a digital array radar framework, constructing a parameterized mapping relationship between multi-dimensional dynamic characteristics and digital array radar multi-channel signal modulation, and outputting target radar echo data meeting group detection, group tracking, and individual perception requirements; Step 3.4, combining target electromagnetic scattering characteristics, signal modulation, and noise models to realize radar scattering electromagnetic characteristic modeling of dynamic UAV clusters.

2. The digital array radar based dynamic unmanned vehicle swarm electromagnetic properties modeling method according to claim 1, characterized in that, In step 3.2, the multi-dimensional dynamic characteristic adaptability extraction based on the time-varying scattering center model is as follows: Step 3.2.1, combining formation pose distribution and the time-varying scattering center model to calculate RCS at each observation time, iterating all observation time sequences to obtain dynamic RCS of the target group, and the expression is as follows: ； wherein, represents the time at which RCS of the drone swarm; Step 3.2.2, for HRRP generation of the dynamic cluster target, based on the real-time characteristics of the cluster configuration and the radar observation geometry, the scattering centers of the cluster target are divided according to distance units, the echo of each distance unit is generated in real time and sequentially superimposed to form HRRP, thereby simulating the distance expansion characteristics of the cluster target in the radar line of sight, providing characteristic basis for cluster detection and distance resolution, and the expression is as follows: ； wherein, denotes the time instant The echo of the first range cell, denotes the time instant The echo of the first range cell time-varying scatterer model; is the number of scatterers in the first range cell, is the amplitude of the first scatterer in the first range cell, is the three-dimensional position vector of the first scatterer in the first range cell, the resulting target one-dimensional range profile is: ； wherein denotes the time target one-dimensional range profile, is the time on the first distance unit; denotes the distance unit number, is the total number of distance units; Step 3.2.3, in the observation of the cluster target by the radar, angle glint is used to simulate the observation deviation of the cluster target in the radar angle dimension, the relationship between the phase gradient and the angle correction is established from the target geometric model to quantify the observation deviation; The total echo is the vector sum of the echoes of each scattering center, and the complex signal form is as follows: ； wherein, Etotrepresents the total echo of the target scattering centers, is the instantaneous phase of the th scattering center, is the total echo amplitude, is the total phase; Phase gradient by wave number The conversion to the line of bias, the offset of the radar line of sight angle in the direction of the target geometric center is shown as follows: ； wherein, is the azimuth angle linear deviation, is the pitch angle linear deviation, is the azimuth angle, is the pitch angle; and , are substituted into the target distance to obtain the angle correction amount of the azimuth angle and the pitch angle , is the distance between the target and the radar; and the updated radar line-of-sight angle, i.e., the azimuth angle and the pitch angle corresponding to the radar apparent center are ; Step 3.2.4, based on dynamic scattering center clustering, the RCS and equivalent position of individual targets in the UAV cluster are obtained, and the specific process is as follows: The RCS of the individual target is as follows: ； The equivalent position is as follows: ； wherein, represents the RCS of the i-th target at time represents the RCS of the i-th target at time represents the RCS of the i-th target at time represents the RCS of the i-th target at time represents the RCS of the i-th target at time represents the RCS of the i-th target at time represents the RCS of the i-th target at time represents the RCS of the i-th target at time represents the RCS of the i-th target at time 3. The digital array radar based dynamic unmanned vehicle swarm electromagnetic properties modeling method according to claim 2, characterized in that, In step 3.3, under the digital array radar framework, a parameterized mapping relationship between multi-dimensional dynamic characteristics and digital array radar multi-channel signal modulation is constructed, and target radar echo data meeting group detection, group tracking, and individual perception requirements are output, and the specific process is as follows: Step 3.3.1, in equivalent point target modeling, the UAV cluster flying in cooperation is simplified as a single scattering point, which is represented by the overall radar scattering cross section and the geometric center position of the cluster as follows: ； wherein, denotes the received signal, denotes the signal after propagation delay caused by target distance, is the current reception time in the radar system, is the propagation delay, is the Doppler shift at the time is the wavelength, is the total RCS of the UAV swarm, is the distance of the radar to the geometric center of the swarm, is the gain of the radar in the direction of the geometric center of the UAV swarm, is the azimuth angle corresponding to the geometric center of the UAV swarm, is the pitch angle corresponding to the geometric center of the UAV swarm;​ Step 3.3.2, an equivalent extended target modeling method is adopted, a radar echo representation model of the cluster target spanning multiple consecutive distance resolution units is constructed by fusing the structural information of HRRP and the dynamic change characteristics of angle glint, and the formula is as follows: ； wherein, denotes the distance of the radar to the geometric center of the drone swarm, denotes the time instant the echo of the distance unit, is a time-delayed pulse function, is the propagation delay, is the gain in the direction of the radar apparent center, and are the corresponding azimuth and elevation angles of the radar apparent center, respectively. The radar echo representation model describes the expansion form of the formation in space distance and the dynamic evolution process of the radar apparent center, wherein the radar apparent center is the angle of the cluster target perceived by the radar system in the observation process; Step 3.3.3, based on the RCS and equivalent position of the individual target obtained by dynamic scattering center clustering, a distributed individual radar echo model is constructed as follows: ； in, Indicates receiving signal, Indicates the target number within the drone swarm. For the target total number, For the first The signal after propagation delay caused by the distance to the target. For the first The propagation delay of each target For the first Target time Doppler shift, For the first RCS of each target For the first The distance between the target and the radar. For radar number Gain in each target direction, and The first The azimuth and elevation angles of each target.

4. The digital array radar based dynamic unmanned vehicle swarm electromagnetic properties modeling method according to claim 3, characterized in that, The combination of the target electromagnetic scattering characteristics, signal modulation and noise model is realized in step 3.4, and the radar scattering electromagnetic characteristics modeling of the dynamic UAV cluster is realized, and the specific implementation is as follows: The radar echo signal is formed by the coupling of the multi-physical effects of target scattering intensity, propagation attenuation, time delay and noise, and the received signal model is expressed as: ； wherein, represents a received signal, is a propagation delay caused by a target distance the signal, is a Doppler shift, is a dynamic drone swarm RCS, is a radar pattern gain, is noise.

5. A digital array radar based dynamic unmanned aerial vehicle swarm electromagnetic properties modeling system, characterized in that, The system is used to realize the dynamic UAV cluster electromagnetic characteristics modeling method based on digital array radar in any one of claims 1-4, and the system comprises a time-varying motion model construction module, a time-varying scattering center model construction module and a radar system signal model construction module, and the functions of each module are as follows: The time-varying motion model construction module sets the radar system parameters by analyzing the performance parameters of the UAV cluster target, establishes the time-varying motion model of the UAV cluster, and obtains the formation pose distribution of the UAV cluster in the global coordinate system; The time-varying scattering center model construction module takes the formation pose distribution as input, decomposes the UAV CAD model into components, performs occlusion analysis under the radar line-of-sight constraint condition, extracts the time-varying scattering center parameters according to the dynamic electromagnetic effect between targets, and updates the scattering center spatial distribution of the UAV cluster in a time sequence recursion manner, and completes the construction of the time-varying scattering center model; The radar system signal model construction module constructs the radar system signal model according to the digital array radar (DAR) parameters, performs multi-dimensional dynamic feature extraction and parameter mapping based on the time-varying scattering center model, completes the construction of the multi-channel signal modulation of the digital array radar and the radar echo of the UAV cluster target, and realizes the radar scattering electromagnetic characteristics modeling of the dynamic UAV cluster.

6. A mobile terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the dynamic UAV cluster electromagnetic characteristics modeling method based on digital array radar in any one of claims 1-4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the steps in the dynamic UAV cluster electromagnetic characteristics modeling method based on digital array radar in any one of claims 1-4.

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