An Unmanned Aerial Vehicle (UAV) Swarm Inductive Countermeasure Method and System Based on "Leading-Type" Group Driven Away

Abstract

The present invention provides a method and system for inducing and countering an unmanned aerial vehicle (UAV) swarm based on "leading type" group expulsion, belonging to the technical field of countering UAV swarms. Aiming at the problem that the subjectivity is relatively strong when selecting countering targets in the prior art, resulting in misjudgment of countering targets, the method of the present invention is directed to a multi-pilot clustering structure UAV swarm. By extracting the key features that can be countered by the UAV swarm, the key individuals that can be countered in the UAV swarm are identified by analyzing the smoothness of the movement trajectory curves. Corresponding countering strategies are set for the cluster leader UAVs and the follower UAVs within the cluster, and a decoy signal model is established. The satellite navigation decoy signal is injected into some of the cluster leader UAVs in the UAV swarm. By utilizing the characteristics of the distributed architecture of the UAV swarm, under the action of in-cluster interactive propagation and inter-cluster traction, the decoy signal is propagated to the entire UAV swarm, thereby realizing the overall expulsion of the UAV swarm. Compared with the traditional method, it has the characteristics of high countering cost-effectiveness and controllable countering effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of countering unmanned aerial vehicle (UAV) swarms, and particularly relates to a method and system for inducing and countering UAV swarms based on "leading-type" swarm driving away. Background Art

[0002] With the development of technologies such as swarm intelligence, system integration, networking communication, and cooperative control, the autonomy and intelligence of UAV swarms have achieved rapid development, and they have broad development prospects in fields such as disaster relief, communication relay, cooperative patrol, and cluster strikes. However, due to the abuse of cluster technology, the risks and security threats brought by UAV swarms have become increasingly prominent, posing a great threat to important personnel, sensitive areas, and key facilities.

[0003] Existing UAV countering methods include dense weapon interception, high-energy laser attack, high-power microwave countering, curtain-type interception, radio interference, data link seizure, and navigation deception, etc.; when countering UAV swarms, the above methods counter the entire UAV swarm by means of fire damage or communication interference against individual UAVs. Whether it is a leaderless structure where all UAVs have equal status or a multi-leader clustering structure with hierarchical order, there are problems such as poor damage effect and low countering cost-effectiveness, and it is difficult to achieve the purpose of all-directional penetration of UAV swarms.

[0004] Nowadays, the multi-leader clustering structure is more common and has more practical value both in biological groups in nature and in actual engineering applications. In order to effectively counter UAV swarms with a multi-leader clustering structure, some key UAVs in the swarm should be selected and countered, and the deception signal should be propagated from some key UAVs to the entire swarm to achieve the overall countering effect of the swarm by taking a point to drive the whole. After retrieval, a Chinese invention patent with the patent number CN113743565B and the name of a method for countering UAV swarms based on a group architecture includes two UAV swarm architectures, namely a non-clustering structure and a multi-clustering structure. The swarm architecture is classified by analyzing the spatial distribution and movement trajectory characteristics of the swarm, and corresponding key UAVs that can be countered are selected for different architectures. The cluster leader aircraft of the multi-clustering structure swarm disclosed therein is divided into multi-level sub-clustering regions according to the characteristics of UAV clustering, and then the cluster leader aircraft is finally determined by combining volume, appearance characteristics, and movement trajectory. However, the method for selecting key UAVs disclosed in the above existing technology only gives a qualitative description, that is, the cluster leader aircraft is obtained through subjective judgment according to information such as the movement characteristics of the UAV swarm as the key UAV that can be countered, which may lead to inaccurate selection or misjudgment, thus reducing the overall countering efficiency. Summary of the Invention

[0005] Aiming at the problems existing in the above-mentioned prior art, the present invention aims to propose a method and system for inducing and countering an unmanned aerial vehicle (UAV) swarm based on "leading" group driving away. By extracting the spatial distribution characteristics of a multi-leader clustering structure UAV swarm, extracting the anti-aircraft characteristics of the swarm, and combining the cooperation and competition, attraction and collision avoidance working mechanisms of the multi-leader clustering structure UAV swarm, a corresponding satellite navigation deception strategy is designed and a corresponding model is constructed. By deceiving the key UAVs in the swarm, the purpose of countering the entire UAV swarm is achieved, which has the advantages of controllable countermeasure effect and small collateral damage.

[0006] The present invention realizes the above technical purpose by adopting the following technical solutions:

[0007] A method for inducing and countering an unmanned aerial vehicle (UAV) swarm based on "leading" group driving away, characterized by comprising the following steps:

[0008] Step1: Monitor the incoming multi-leader clustering structure UAV swarm in real time to obtain the spatial distribution characteristics of the UAV swarm;

[0009] Step2: Based on the spatial distribution characteristics of the UAV swarm, identify and select the cluster leader as the key UAV that can be countered;

[0010] Step3: Develop a countermeasure strategy for the internal structure of the cluster and the interaction relationship between clusters for the key UAV that can be countered;

[0011] Step4: Establish a navigation deception signal model for the cluster leader and the followers within the cluster, and issue a navigation deception signal to the key UAV;

[0012] Step5: Determine the countermeasure effect according to whether the movement trajectory of the incoming UAV swarm deviates from the expected trajectory. If it deviates, the countermeasure ends; otherwise, turn to Step1.

[0013] To achieve the above purpose, further, the process of obtaining the spatial distribution characteristics of the UAV swarm described in Step1 includes:

[0014] Step101: Simultaneously obtain multi-source image information such as infrared, radar, and / or visible light imaging of the UAV swarm;

[0015] Step102: For the obtained multi-source image information of the UAV swarm, use an information fusion algorithm based on discrete wavelet transform to decompose the image into a combination of a low-frequency image and a detail (high-frequency) image, extract the structural information and detail information of the original image, realize multi-source information fusion and image reconstruction, and avoid the problems of low image contrast and lack of details in the average image fusion algorithm;

[0016] Step103: Utilize the redundancy and complementarity of information among the source images to improve the accuracy of detection information, and then calculate the spatial distribution information of the UAV swarm.

[0017] Furthermore, the information fusion algorithm of discrete wavelet transform in the above Step102 is specifically as follows:

[0018] Step1021: Perform wavelet transform on the multi-source images to obtain a low-frequency component and three high-frequency components in different directions after decomposition, and obtain their respective wavelet decomposition coefficients.

[0019] Step1022: Fuse the low-frequency component and high-frequency components after wavelet transform decomposition to obtain new fused wavelet decomposition coefficients. For the fusion of the low-frequency component, a calculation method based on the local energy of the image is adopted, and for the fusion of the high-frequency component, a selection rule based on the maximum value is adopted.

[0020] Step1023: Perform inverse wavelet transform reconstruction on the new fused wavelet decomposition coefficients obtained in Step1022 to obtain the fused image.

[0021] Furthermore, the process of identifying and selecting the key UAVs that can be countered described in Step2 includes:

[0022] Step201: According to the obtained spatial distribution characteristics of the UAV swarm, plot the velocity-time, position-time, and motion trajectory curves of each UAV.

[0023] Step202: Set a threshold λ for the smoothness of the motion trajectory curves obtained above, and select the UAVs with smoothness less than the threshold λ as candidates for key UAVs.

[0024] Step203: Utilize the volume and appearance characteristics of the cluster leader aircraft, take the suspicious target position as the initial point, track and record the candidate key UAVs, and eliminate the UAVs that obviously do not conform to the motion characteristics of the cluster leader aircraft to obtain the cluster leader aircraft as the key UAVs that can be countered.

[0025] Furthermore, the process of formulating the countermeasure strategies for the intra-cluster architecture and inter-cluster interaction relationships described in Step3 includes:

[0026] Step301: In the countermeasure strategy for inter-cluster relationships based on the motion trajectory, the decoy signal acts on the cluster cluster leader aircraft and is distributed and interactively propagated within it.

[0027] Step302: Utilize the traction effect between different clusters, and through the interaction relationship between the cluster leader aircraft of each cluster, spread the influence of the decoy signal to the cluster that has an interactive traction effect with the cluster and within the cluster It is propagated in the form of local interaction;

[0028] Step303: The decoy signal spreads to the entire UAV swarm through the interaction of adjacent UAVs within each sub-cluster and the cooperation and traction between different sub-clusters, inducing the UAV swarm to deviate from the expected movement trajectory to deviate from the mission objective, thus achieving an effective countermeasure against the UAV swarm with a multi-leader sub-cluster structure.

[0029] Furthermore, the specific process of establishing the navigation decoy signal model described in Step4 includes:

[0030] Step401: The navigation decoy signal models established for the cluster leader aircraft and the followers within the cluster are as follows:

[0031]

[0032]

[0033] Among them, represents the sub-cluster where UAV i is located, represents the remaining sub-clusters except for the cluster In formula (1), δ i (t) represents the total influence on UAV i in the bee swarm under the action of the decoy signal at time t, and respectively represent the influence of the decoy signal on UAV i in the cluster and the cluster on the cluster In formula (2), w i0 (t), w ij (t) respectively represent the action weights of the cluster leader UAV and UAV j within the cluster on UAV i at time t, is the decoy signal at time t, and respectively represent the states of the cluster leader UAV, UAV i and UAV j within the cluster at time t, and respectively represent the formation vectors of UAV i and UAV j within the cluster at time t, represents the set composed of all UAVs in the cluster In formula (3), d i is the restraint effect gain, and represent the states of UAV i and UAV j within the cluster at time t respectively, and represent the cluster The formation vector of UAV i and UAV j within the medium cluster represents the set of other UAVs in the UAV swarm except ; represents the cluster at time t used for the reference state of the traction effect on the cluster , affected by the decoy signal in the cluster ; ;

[0034] Step402: Without triggering the fault tolerance mechanism of the UAV swarm, spread the decoy signal to the entire UAV swarm through distributed information interaction and the mutual traction effect between different clusters, induce the UAV swarm to deviate from the expected motion trajectory, and finally achieve effective countermeasures against the UAV swarm.

[0035] The present invention also proposes a UAV swarm induced countermeasure system based on "leading" group expulsion, including

[0036] an air defense identification and surveillance system for real-time monitoring of the spatial distribution characteristics of a multi-leader cluster-structured UAV swarm;

[0037] an image processing system for reconstructing and processing multi-source image information of a multi-leader cluster-structured UAV swarm;

[0038] a strategy formulation system for formulating countermeasure strategies for cluster leader UAVs and followers within the cluster;

[0039] a model establishment system for establishing a decoy signal model for cluster leader UAVs and followers within each cluster.

[0040] Furthermore, the strategy formulation system specifically includes

[0041] a distributed interaction system for the interactive propagation of decoy signals within the cluster ;

[0042] a diffusion traction interaction system for spreading the decoy signal from the cluster to the cluster and performing local interactive propagation within the cluster ;

[0043] an induced countermeasure system for inducing the UAV swarm to deviate from the expected motion trajectory and achieving overall countermeasures.

[0044] The present invention also provides an electronic device, including a memory and a processor. The memory stores computer instructions, and the processor is used to run the computer instructions stored on the memory to implement any one of the above methods.

[0045] The present invention also provides a computer-readable storage medium storing computer instructions for causing a computer to execute any one of the methods described above.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. By adopting an information fusion algorithm based on discrete wavelet transform to fuse and reconstruct the multi-source image information of the unmanned aerial vehicle (UAV) swarm obtained, compared with the weighted average fusion algorithm, the root mean square error is reduced by 22.9% while the information entropy remains basically unchanged, avoiding the problems of low image contrast and lack of rich details in the average image fusion algorithm, improving the signal-to-noise ratio of the fused image and the accuracy of detection information, and enabling more accurate acquisition of the spatial distribution information of the UAV swarm through calculation.

[0048] 2. Based on the architecture characteristics of the multi-leader clustering structure UAV swarm, an adversarial strategy for the intra-cluster architecture and inter-cluster interaction relationship is set. Taking the cluster leader UAV as the key individual to be countered and the motion trajectory characteristics as the entry point of the adversarial feature, a navigation deception signal model for the cluster leader UAV and the follower UAVs within each cluster is established with this adversarial strategy. While considering the propagation of the deception signal among the clusters of the UAV swarm, the influence of the signal on the UAVs within the clusters is also considered. By applying the deception signal to some of the cluster leader UAVs, through the intra-cluster distributed interaction propagation and inter-cluster interaction traction effect, the propagation of the deception signal affects the spatial form of the entire UAV swarm. When the deception signal propagates among the clusters, each cluster causes internal task conflicts within the UAV group through the competition - cooperation mechanism, resulting in chaos in each cluster. When the deception signal propagates within the cluster, the UAVs within the cluster cannot maintain the predetermined formation due to the target - attraction and collision - avoidance - repulsion principles, thus achieving the effect of overall countermeasure by countering the cluster leader UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below.

[0050] Figure 1 It is a flowchart of a method and system for inducing and countering a UAV swarm based on "leading - type" group driving away of the present invention;

[0051] Figure 2 It is a schematic diagram for analyzing the behavioral characteristics of a multi - leader clustering structure UAV swarm of the present invention;

[0052] Figure 3 It is a design block diagram of a method for countering a multi - leader clustering structure UAV swarm of the present invention;

[0053] Figure 4 It is a schematic diagram of the expected motion trajectory and the deviated trajectory of the relationship within the UAV group under the countermeasure method of the present invention;

[0054] Figure 5 It is a schematic diagram of the expected motion trajectory and deviation trajectory of the relationship between clusters in the countermeasure method of the present invention. Specific embodiments

[0055] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0056] Refer to Figures 1-5 As shown, the present invention proposes a method and system for inducing countermeasures for an unmanned aerial vehicle (UAV) group based on "leading" group driving away. The method specifically includes the following steps

[0057] Step1: Use the ground air defense identification and surveillance system to monitor the incoming multi-leader cluster-structured UAV group in real time, obtain and fuse multi-source image information such as infrared, radar, and / or visible light imaging of the incoming multi-leader cluster-structured UAV group, and obtain the spatial distribution characteristics of the UAV group;

[0058] Step2: Based on the motion trajectory and spatial distribution characteristics of the multi-leader cluster-structured UAV group, identify and select the cluster leader aircraft as the key UAV that can be countered;

[0059] Step3: Take the motion trajectory characteristics of the key UAV as the entry point for countermeasure characteristics, and formulate countermeasure strategies for the intra-cluster architecture and the inter-cluster interaction relationship;

[0060] Step4: Establish a navigation deception signal model for the cluster leader aircraft and the follower aircraft within the cluster, and send a navigation deception signal to the key UAV;

[0061] Step5: Determine the countermeasure effect by observing whether the motion trajectory of the incoming UAV group in the air defense identification and monitoring system deviates from the expected trajectory. If it deviates, the countermeasure ends; otherwise, turn to Step1.

[0062] Preferably, the process of obtaining the spatial distribution characteristics of the UAV group in Step1 includes:

[0063] Step101: Use the own air defense identification and monitoring system to simultaneously obtain multi-source image information such as infrared, radar, and / or visible light imaging of the UAV group;

[0064] Step102: After collecting the above multi-source image information of the UAV swarm, use an information fusion algorithm based on discrete wavelet transform to decompose the image into a combination of a low-frequency image and a detail (high-frequency) image, extract the structural information and detail information of the original image, realize multi-source information fusion and image reconstruction, and improve the signal-to-noise ratio of the fused image;

[0065] The fusion method is as follows: First, perform wavelet transform on the source image to obtain one low-frequency component and three high-frequency components in different directions after decomposition. The low-frequency component can be further decomposed to finally form a multi-resolution structure. Second, fuse the low-frequency component and high-frequency components after wavelet transform decomposition. For the fusion of the low-frequency component, adopt a calculation method based on the local energy of the image, and for the fusion of the high-frequency component, adopt a selection rule based on the maximum value. Third, perform inverse wavelet transform on the wavelet components obtained by fusion to reconstruct the fused image.

[0066] Step103: Utilize the redundancy and complementarity of information between each source image to improve the accuracy of detection information, and then calculate the spatial distribution information of the UAV swarm.

[0067] The incoming UAV swarm system of the present invention is a multi-leader clustered UAV swarm. Each sub-cluster can be regarded as an independent system. Tasks are allocated through information interaction and negotiation between different sub-clusters. Each sub-cluster contains one or more cluster leader UAVs. The cluster leader UAV is responsible for the communication between the followers within the cluster and the interaction between the followers within the cluster and the UAVs of other clusters. The followers within the cluster use the cluster leader UAV as a reference object to adjust their own states in real time to achieve the purpose of target tracking and swarm control.

[0068] Preferably, the process of identifying and selecting the key UAVs that can be countered in Step2 includes:

[0069] Step201: According to the above-obtained spatial distribution characteristics of the UAV swarm, draw the velocity-time, position-time, and motion trajectory curves of each UAV;

[0070] Step202: Set a threshold λ for the smoothness of the above-obtained motion trajectory curve, and select the UAVs with smoothness less than the threshold λ as candidates for key UAVs;

[0071] Step203: Utilize the volume and appearance characteristics of the cluster leader UAV, start from the suspicious target position, track and record the candidate key UAVs, and eliminate the UAVs that obviously do not conform to the motion characteristics of the cluster leader UAV to obtain the cluster leader UAV as the key UAV that can be countered.

[0072] Preferably, the process of formulating the confrontation strategy for the intra-cluster system structure and the inter-cluster interaction relationship in Step3 includes:

[0073] Step301: In the inter-cluster relationship confrontation strategy based on the motion trajectory, the decoy signal acts on the cluster leader aircraft of the cluster and is distributed and interactively propagated inside it; and is distributed and interactively propagated inside it;

[0074] Step302: Utilize the traction effect between different clusters, and through the interaction relationship between the cluster leader aircraft of each cluster, spread the influence of the decoy signal to the cluster where the interactive traction effect occurs, and propagate inside the cluster in the form of local interaction;

[0075] Step303: The decoy signal spreads to the entire UAV swarm through the interaction of adjacent UAVs within each sub-cluster and the cooperation and traction effects between different sub-clusters, inducing the UAV swarm to deviate from the expected motion trajectory and thus deviate from the mission objective.

[0076] Since there is a competitive relationship between the clusters of each sub-cluster, during the flight of the UAV swarm, each sub-cluster triggers an online mission planning mechanism, and different clusters with a competitive relationship move along completely opposite trajectories. When a certain sub-cluster is affected by the decoy signal, the competing cluster will receive the opposite decoy signal effect, thereby triggering task conflicts within the aircraft group, causing each sub-cluster to fall into chaos, and finally it is difficult for the clusters to maintain the expected formation and deviate from the expected trajectory, thus achieving an effective confrontation of the multi-leader sub-cluster structure UAV swarm.

[0077] Preferably, the specific process of establishing the navigation decoy signal model in Step4 includes:

[0078] Step401: The navigation decoy signal model established for the cluster leader aircraft and the followers within the cluster is as follows:

[0079]

[0080]

[0081] In formula (1), δ i (t) represents the total influence received by the UAV i in the bee swarm under the action of the decoy signal at time t, and respectively represent the influence of the decoy signal on the cluster and the cluster on the UAV i in the cluster ; in formula (2), w i0 (t), w ij (t) respectively represent the action weights of the cluster leader UAV and the UAV j within the cluster on the UAV i at time t, is the decoy signal at time t, and respectively represent the cluster at time t The states of the cluster head UAV, the in-cluster UAV i, and the UAV j and respectively represent the formation vectors of the in-cluster UAV i and the UAV j in the cluster at time t ; denotes the set composed of all UAVs in the cluster ; In formula (3), d i is the gain of the pinning effect and represent the states of the in-cluster UAV i and the UAV j in the cluster at time t respectively ; and represent the formation vectors of the in-cluster UAV i and the UAV j in the cluster at time t ; denotes the set of other UAVs in the UAV swarm except ; represents the reference state for the towing effect of the cluster on the cluster , which is affected by the spoofing signal in the cluster ;

[0082] While considering the propagation of the spoofing signal among the clusters of the UAV swarm, this model also considers the influence of the signal on the UAVs within the clusters; In the above model, the first term on the right side of the equal sign describes the influence of the spoofing signal on the cluster leader aircraft of the cluster [[ID=null]] and the attracting effect of the cluster leader aircraft on the follower aircraft i within the cluster, and the second term represents the distributed interaction between adjacent UAVs within the cluster ; the first term on the right side of the equal sign represents the pinning effect of the cluster on the cluster , which is affected by the spoofing signal in the cluster ; the second term on the right side of the equal sign represents the distributed interaction between adjacent UAVs within the cluster ;

[0083] Step402: Without triggering the fault tolerance mechanism of the UAV swarm, through the distributed information interaction propagation and the mutual towing effect between different clusters, spread the spoofing signal to the entire UAV swarm, induce the UAV swarm to deviate from the expected motion trajectory, and finally achieve an effective countermeasure against the UAV swarm.

[0084] An anti - drone swarm induction counter - measure system based on "leading - type" group driving away of the present invention includes an air defense identification and surveillance system for real - time monitoring of the spatial distribution characteristics of a multi - leader clustering - structured drone swarm; an image processing system for reconstructing and processing the multi - source image information of the multi - leader clustering - structured drone swarm; and a model - building system for building a decoy signal model for the cluster leader aircraft and the followers within each cluster.

[0085] Embodiment 1

[0086] The drones in the multi - leader clustering structure can form multiple cooperating clusters according to different mission objectives or their own regions. The cluster - head nodes in each cluster make decisions on the tasks of their own clusters. The cluster - head nodes are responsible for the communication between the nodes within the cluster and the interaction between the nodes within the cluster and the nodes of other clusters. The nodes within the cluster regulate their own states in real - time with the cluster - head node as the reference object. The multi - leader clustering - structured drone swarm follows the principles of "attraction and repulsion, cooperation and competition" and other behavioral characteristics to complete behaviors such as the formation, maintenance, and switching of the overall formation of the drone swarm.

[0087] In the Figure 5 shown multi - cluster - structured drone swarm, there are a total of three sub - clusters, and each sub - cluster is composed of a cluster - leader aircraft and followers within the cluster. Inside the sub - cluster, the cluster - leader aircraft sends its own state information to the followers within the cluster, and the followers within the cluster form a corresponding regular pentagon mission formation according to the state and instructions of the cluster - leader aircraft. According to the principles of target attraction and collision - avoidance repulsion among the cluster - leader aircraft of each sub - cluster, the entire drone swarm forms a triangular mission formation according to the mission situation. Due to the real - time changes in the mission scenario and mission situation, there are also cooperation and competition relationships among the cluster - leader aircraft due to task allocation. Through competition, the overall effectiveness of the entire drone swarm is maximized.

[0088] The present invention applies false navigation decoy signals to the cluster - leader aircraft of the multi - leader clustering - structured drone swarm. As Figure 4 shown, inside the sub - cluster area, the navigation decoy signal takes over the navigation information of the cluster - leader unmanned aircraft. Without causing the fault - tolerance response mechanism of the drone swarm, the cluster - leader aircraft is deviated from the expected mission target position. The followers within the cluster use the actual position of the cluster - leader aircraft as a reference and do not directly receive virtual target information. Therefore, under the action of distributed interaction and collaborative traction, while maintaining the expected sub - cluster formation, the followers within the cluster follow the cluster - leader aircraft to deviate from the expected mission trajectory, and finally achieve the directional driving away of the sub - cluster based on the movement trajectory; as Figure 5As shown, affected by the interaction, attraction, and collision avoidance features among the cluster leader aircraft, the decoy signals gradually spread among the adjacent cluster leader aircraft of different clusters, resulting in overall chaos in the aircraft group. Due to the competitive relationship among clusters, during the flight of the UAV group, each sub-cluster triggers an online task planning mechanism. Different clusters with competitive relationships move along completely opposite trajectories. When a certain sub-cluster is affected by decoy signals, the competing cluster will be affected by opposite decoy signals, thereby triggering internal task conflicts within the aircraft group and causing each sub-cluster to fall into chaos. For example, Figure 5 there are significant deviations between the expected target trajectory and task assignment and the actual target trajectory and task assignment in Figure 5 , deviating from the expected trajectory. Eventually, it is difficult for the clusters to maintain the expected formation to execute the target task. By deceiving some key cluster leader aircraft, the effect of countering the entire UAV group is achieved, with precise countering targets and high countering results.

[0089] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for inducing countermeasures against a swarm of unmanned aerial vehicles based on "leading-style" group driving away, characterized in that, It includes the following steps: Step1: Monitor the incoming multi-leader clustering UAV swarm in real time to obtain the spatial distribution characteristics of the UAV swarm; Step2: Based on the spatial distribution characteristics of the UAV swarm, identify and select the cluster leader UAV as the key UAV that can be countered; Step3: Develop countermeasure strategies for the intra-cluster architecture and inter-cluster interaction relationships for the key UAV that can be countered; Step4: Establish a navigation deception signal model for the cluster leader UAV and the followers within the cluster, and issue navigation deception signals to the key UAV; Step5: Determine the countermeasure effect based on whether the movement trajectory of the incoming UAV swarm deviates from the expected trajectory. If it deviates, the countermeasure ends; Otherwise, turn to Step1; The process of identifying and selecting the key UAV that can be countered described in Step2 includes: Step201: According to the obtained spatial distribution characteristics of the UAV swarm, draw the velocity-time, position-time, and movement trajectory curves of each UAV; Step202: Set a threshold λ for the smoothness of the obtained movement trajectory curves, and select the UAVs with smoothness less than the threshold λ as the candidate key UAVs; Step203: Use the volume and appearance characteristics of the cluster leader UAV, starting from the suspicious target position, track and record the candidate key UAVs, and eliminate the UAVs that obviously do not conform to the movement characteristics of the cluster leader UAV, and obtain the remaining cluster leader UAVs as the key UAVs that can be countered; The process of developing countermeasure strategies for the intra-cluster architecture and inter-cluster interaction relationships described in Step3 includes: Step301: In the cluster - to - cluster relationship confrontation strategy based on the motion trajectory, the decoy signal acts on the cluster leader aircraft of the cluster and is distributed and interactively propagated within it; ​ Step302: By leveraging the traction effect between different clusters and through the interaction relationships among the leading aircraft of each cluster, spread the influence of the decoy signal to the clusters that have interactive traction with it and propagate it within the clusters in the form of local interactions; Step303: The deception signals spread to the entire UAV swarm through the interaction between adjacent UAVs within each cluster and the cooperation and traction between different clusters, inducing the UAV swarm to deviate from the expected movement trajectory to deviate from the mission target, and realizing the effective countermeasure against the multi-leader clustering UAV swarm; The specific process of establishing the navigation deception signal model described in Step4 includes: Step401: The navigation deception signal model established for the cluster leader UAV and the followers within the cluster is as follows: in, Representative drone The cluster where Represents cluster removal For the remaining clusters except , in formula (1), express Time decoy signals act as drones in bee swarms The total impact, and Respectively Cluster under the action of time deception signal and clusters Cluster Medium UAV Influence; In formula (2), Respectively Cluster-head drones and cluster-inside drones drones The weight of the effect, for The deceptive signal of the moment, 、 and Respectively Moment Cluster UAVs in clusters and UAVs within clusters and drones status, and Respectively Moment Cluster UAVs in the cluster and drones The formation vector, Represents a cluster The set of all drones; in formula (3), For the restraining effect gain, and Respectively Moment Cluster UAVs in the cluster and drones status, and Respectively Moment Cluster UAVs in the cluster and drones The formation vector, Indicates that the drone group A collection of other drones, express Moment Cluster For clustering The reference state of traction, affected by cluster The impact of the decoy signal ; Step402: Without triggering the fault tolerance mechanism of the UAV swarm, through distributed information interaction propagation and the mutual traction between different clusters, the deception signals are spread to the entire UAV swarm, inducing the UAV swarm to deviate from the expected movement trajectory, and finally realizing the effective countermeasure against the UAV swarm.

2. The method for inducing and countering an unmanned aerial vehicle (UAV) swarm based on "leading type" group driving away according to claim 1, wherein The process of obtaining the spatial distribution characteristics of the UAV swarm described in Step1 includes: Step101: Simultaneously obtain multi-source image information of infrared, radar, and / or visible light imaging of the UAV swarm; Step102: For the obtained multi-source image information of the UAV swarm, adopt an information fusion algorithm based on discrete wavelet transform to decompose the image into a combination of low-frequency images and detail images, extract the structural information and detail information of the original image, and realize multi-source information fusion and image reconstruction; 3. The method for inducing and countering an unmanned aerial vehicle swarm based on "leading" group driving away according to claim 2, wherein Step103: Utilize the redundancy and complementarity of information between each source image to improve the accuracy of detection information, and then calculate the spatial distribution information of the UAV swarm. The information fusion algorithm of discrete wavelet transform in the above Step102 is specifically as follows: Step 1021: Perform wavelet transform on the multi-source image to obtain one low-frequency component and three high-frequency components in different directions after decomposition, and calculate their respective wavelet decomposition coefficients; Step 1022: Fuse the low-frequency component and high-frequency components after wavelet transform decomposition to obtain new fused wavelet decomposition coefficients. For the fusion of the low-frequency component, a calculation method based on the local energy of the image is adopted, and for the fusion of the high-frequency component, a selection rule based on the maximum value is adopted; Step 1023: Perform inverse wavelet transform on the newly obtained fused wavelet decomposition coefficients to reconstruct the fused image.

4. A drone swarm induction countermeasure system based on "leading type" group expulsion, characterized in that, This system is used to implement the method for inducing and countering an unmanned aerial vehicle (UAV) swarm based on "leading-style" group driving away as described in any one of claims 1-3. This system includes: An air defense identification and surveillance system for real-time monitoring of the spatial distribution characteristics of a multi-leader clustered UAV swarm; An image processing system for reconstructing and processing the multi-source image information of a multi-leader clustered UAV swarm; A strategy formulation system for formulating countermeasure strategies for the cluster leader aircraft and the followers within the cluster; A model establishment system for establishing a decoy signal model for the cluster leader aircraft and each follower within the cluster.

5. The drone swarm induced countermeasure system based on "leading type" group driving away according to claim 4, characterized in that The strategy formulation system includes Distributed interaction system for luring signals for interactive propagation within a cluster Internal interactive propagation; Diffusion traction interaction system for luring signals from a cluster to diffuse to the cluster and locally interactively propagate within the cluster; ​ An induction and countermeasure system for inducing the UAV swarm to deviate from the expected movement trajectory to achieve overall countermeasures.

6. An electronic device, comprising a memory and a processor, characterized in that, Computer instructions are stored on a memory, and a processor is used to run the computer instructions stored on the memory to implement the steps of the method for inducing and countering an unmanned aerial vehicle (UAV) swarm based on "leading-style" group driving away as described in any one of claims 1-3.

7. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the steps of the method for inducing and countering an unmanned aerial vehicle (UAV) swarm based on "leading-style" group driving away as described in any one of claims 1-3.

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