A method for range estimation in adversarial unmanned swarm communication based on motion behavior

Abstract

This invention discloses a method for estimating the communication range of adversarial drone swarms based on motion behavior. It analyzes adversarial drone swarms through observation and interaction, ensuring the observability of the drone communication range. Based on a disruptor intrusion method, a numerical calculation framework for generating disruptor traject ...ies is designed to push some drone targets into the "observable zone." Simultaneously, it considers the position and energy changes of the drone swarm before and after the disruption, minimizing the impact of the disruptor on the drone swarm. This invention solves the potential unobservability of drone swarm communication range, enabling the understanding of adversarial drone swarms by utilizing their dynamic behavior in the absence of prior knowledge.

Description

Technical Field

[0001] This invention belongs to the field of unmanned systems technology, and particularly relates to a method for estimating the range of adversarial unmanned swarm communication based on motion behavior. Background Technology

[0002] With the increasing application of autonomous drone swarms, the importance of analyzing swarm behavior is becoming increasingly prominent. Typical objectives include estimating communication topology, calculating inter-drone cooperation strategies, inferring swarm structure, and identifying intentions. Among these swarm metrics, communication range plays a crucial role in generating swarm motion responses to different external stimuli. In traditional swarm control algorithms, such as the artificial potential field method, each drone calculates its actions based on the states of its neighboring drones received within its communication range. Therefore, accurately estimating the communication range of adversarial drone swarms provides key insights into swarm cognition.

[0003] Research related to understanding the characteristics of unmanned swarm communication networks mainly focuses on topology inference, which can be divided into two main categories: cooperative inference and non-cooperative inference. Cooperative topology inference allows access to the unmanned swarm network, and the monitoring system can exchange data with the network to calculate the node connectivity graph of the network under test. However, due to unknown message encryption or authentication mechanisms, adversarial unmanned swarm wireless networks are often difficult to access, thus non-cooperative topology inference is the primary method for analysis. In non-cooperative topology inference, the monitoring system and the monitored network are independent of each other, and the monitored network can only be studied from an external perspective based on passive observation. A common method is based on observation of communication behavior, that is, monitoring the communication signals between UAVs, then constructing the corresponding signal feature sequences, and then inferring the communication relationships between nodes. Although the above methods have been successful in various simulations and experimental tests, they still have some limitations in the study of communication range estimation. In practical applications, the estimation of accurate communication range values ​​differs from topology inference because once the communication range of each UAV is known, the swarm communication topology can be inferred from the observed UAV positions; however, the reverse is not true, and the communication range cannot be inferred from the communication topology. Secondly, when the communication range of each drone is small and the sensors are outside the communication range, the data exchange within the drone swarm cannot be measured. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a method for estimating the communication range of an unmanned aerial vehicle (UAV) swarm based on motion behavior. This method analyzes adversarial UAV swarms through observation and interaction, ensuring the observability of the UAV communication range. Based on a disruptor intrusion method, a numerical calculation framework for generating disruptor trajector trajectories is designed to push some UAV targets into the "observable zone," while simultaneously considering the moving targets and their impact on the enemy swarm, thus minimizing the impact on the adversarial UAV swarm.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for estimating the communication range of an adversarial unmanned swarm based on motion behavior, comprising the following steps:

[0006] (1) Establish an unmanned bee colony system and determine the optimal steady-state system structure;

[0007] (2) Determine the edge points based on the optimal steady-state system, divide the observable area of ​​the communication range, and introduce interferers to interfere with the unmanned bee colony at different edge points;

[0008] (3) Construct the optimal interference movement planning problem and estimate the communication range of the unmanned swarm;

[0009] (4) Compare the difference between the estimated and actual range of the unmanned swarm communication range, and select the optimal speed of the interfering agent based on the degree of interference of the interfering agent on the unmanned swarm.

[0010] Furthermore, the unmanned swarm system in step (1) is based on an artificial potential field model and consists of n unmanned aerial vehicles (UAVs) and m virtual leaders. Each UAV in the system is affected by three control forces, namely the interaction force between adjacent UAVs, the force exerted by the virtual leader on the UAV, and the controlled dissipation force.

[0011] Furthermore, the interaction force between adjacent UAVs is used to force the desired inter-UAV spacing; the interaction force f I The magnitude and corresponding potential energy V I All depend on the distance x between drone i and its neighboring drone j. ij It is expressed by the following formula:

[0012]

[0013] Where α is the scalar control gain, and d0 and d1 are scalar constants; since the interaction force f only occurs when the distance is less than d1. I Since it is not zero, in the actual implementation of the artificial potential field model, d1 is usually equal to the communication range;

[0014] The virtual leader of the unmanned swarm will also exert an effect of size f. h The force, with potential energy V h The expression is as follows:

[0015]

[0016] Among them, h ik It is the distance between the i-th drone and the k-th virtual leader;

[0017] To ensure the stability of the swarm system, i.e., that all drones move at the desired speed, a controlled dissipative force also needs to be applied. Where K is a positive constant;

[0018] As the drone swarm moves at velocity v0(t) along with the virtual leader, the control force u applied to the i-th drone... i Defined as:

[0019]

[0020]

[0021] Its motion model for unmanned bee swarms is defined as:

[0022]

[0023] Where x is the state variable vector of the unmanned bee colony, u is the external control input, p is the control parameter of the internal control strategy, t is time, and f is the dynamic equation of the bee colony's state variables.

[0024] Furthermore, when the unmanned swarm reaches a steady state, the swarm members maintain the same speed and the relative distance remains unchanged, meaning that the control force of each drone is zero; the system steady state includes the following two types:

[0025] (a) Normal steady state: Under normal steady state, the attractive and repulsive forces of each drone are in equilibrium, and the total control force is zero;

[0026] (b) Optimal steady state: In the optimal steady state, the force between any two UAVs is zero, that is, there is no attractive or repulsive force between any UAVs. Normal steady state may have various cluster formations, but the optimal steady state always has a fixed cluster formation structure, that is, the distance between UAVs is a fixed value d0, which makes it easy for the interferer to interfere with the edge points of the optimal steady state system with a fixed structure.

[0027] Furthermore, the observable range of the unmanned swarm communication in step (3) is defined as follows:

[0028] Only true values ​​can trigger the observation of group dynamics. The interferer needs to move at least one drone from inside the communication range of another drone to the outside, or from the outside to the inside, that is, push some drone targets into the "observable area".

[0029] Under optimal steady-state conditions, the distance between two adjacent UAVs is a fixed value d0. The value of d0 can be inferred from the steady-state formation, and there exists... The structure is given by d1, where d1 is the communication distance to be estimated. Therefore, the two interference scenarios mentioned above can be created by appropriately scaling the scale. The interferer needs to push one of the two drones that were originally d0 apart in the optimal steady state to a distance of d0. Alternatively, the distance between them in the optimal steady state could be greater than or equal to... One of the two drones advanced to within d0 of each other.

[0030] Furthermore, the method for estimating the communication distance d1 is as follows:

[0031] Let parameter p be a variable, and generate the corresponding observation trajectory. calculate The deviation from the original trajectory y is minimized to obtain the optimized parameters. The value, the time interval [t1, t2] of the original trajectory y, is as follows:

[0032] Solve

[0033] minimize

[0034]

[0035] in, This represents the deviation between two trajectories. The optimization algorithm used here is the biologically inspired evolutionary algorithm CMA-ES.

[0036] Furthermore, the interfering party needs to satisfy the following assumptions and optimization conditions:

[0037] (a) Set the speed of the interfering party after takeoff to a constant, thereby simplifying the intrusion process, and there is no need to change direction or speed during flight;

[0038] (b) In order to ensure that the communication range is observable, the jammer needs to move the target UAV to the “observable area” to achieve at least one observable condition;

[0039] (c) To minimize the degree of interference, the comparison of the position and energy of the drone swarm before and after the interference should be used as the basis for judgment; two indicators are used to quantitatively analyze the degree of impact on the drone swarm: position-based indicators and energy-based indicators; since the virtual leader is the key control point of the swarm, the degree of interference in the position-based indicators is defined as the change in the relative position between each drone and the virtual leader, as follows:

[0040]

[0041] Among them I pos It is an indicator based on the degree of influence of changes in the position of the bee colony. Dist(i,vir,t) represents the distance between drone i and the virtual leader at time t, n is the number of drones in the drone colony, T represents the time when the interferer leaves the drone colony, and t0 represents the time when the interferer begins to interfere.

[0042] The energy-based index utilizes the Lyapunov function Φ defined in the artificial potential field method, which is the sum of total kinetic energy and artificial potential energy, to quantify the influence, as defined below:

[0043] I en =Φ(t0+T)-Φ(t0);

[0044]

[0045] Among them I en It is an index based on the degree of influence of changes in bee colony energy, where k = 1, ..., N represents the discrete observation time index; v0 is the speed of the virtual leader, and m is the total number of virtual leaders;

[0046] Based on the above descriptions and formulas, the optimal interactive control problem can be summarized as minimizing... in Let represent the loss function of the target drone i, and let TS represent the set of target drones that the interferer uses to interfere with the drone swarm.

[0047] The present invention also provides a range estimation device for adversarial unmanned swarm communication based on motion behavior, comprising the following modules:

[0048] Establish system modules: Establish an unmanned bee colony system and determine the optimal steady-state system structure;

[0049] Interference module: Based on the optimal steady-state system, edge points are determined, the observable area of ​​the communication range is divided, and interfering agents are introduced at different edge points to interfere with the unmanned bee colony;

[0050] Estimation module: Constructs the optimal motion planning problem for the interfering party and estimates the communication range of the unmanned swarm;

[0051] Screening module: Compare the difference between the estimated and actual communication range of the unmanned swarm, and select the optimal speed of the interferer based on the degree of interference of the interferer to the unmanned swarm.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This invention proposes an attention-based method for unmanned aerial vehicle (UAV) swarm motion path planning. For UAV swarm models based on the artificial potential field method, a comprehensive analysis of UAV motion behavior estimation methods relying solely on location information is conducted, thereby accurately estimating the communication range of UAVs in adversarial UAV swarms. Delineating the observable communication range ensures the observability of the UAV communication range, and establishing a quantitative calculation framework based on interference methods minimizes the impact on the adversarial UAV swarm while maintaining the observability of the UAV communication range. Attached Figure Description

[0054] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0055] Figure 1 This is a map showing the observable area of ​​the communication range of an unmanned bee colony under optimal steady-state conditions.

[0056] Figure 2 This is a flowchart of an adversarial unmanned swarm communication range estimation method based on motion behavior. Detailed Implementation

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0058] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0059] The following is in conjunction with the appendix Figure 1 and attached Figure 2 The present invention will be further explained and illustrated with specific embodiments:

[0060] The first aspect of the present invention: The present invention provides a method for estimating the range of adversarial unmanned swarm communication based on motion behavior, comprising the following steps:

[0061] Step 1: Establish an unmanned swarm system based on an artificial potential field model, consisting of n drones and m virtual leaders, where each drone is influenced by three control forces:

[0062] One type is the interaction force between adjacent drones, used to force the required inter-drone spacing. The interaction force f I The magnitude and corresponding potential energy V I All depend on the distance x between drone i and its neighboring drone j. ij They are represented by the following formulas:

[0063]

[0064]

[0065] Where α is the scalar control gain, and d0 and d1 are scalar constants. Since the interaction force f only exists when the distance is less than d1... I Since it is not zero, in the actual implementation of the artificial potential field model, d1 is usually equal to the communication range.

[0066] Similarly, a virtual leader near the drone will also exert an effect of size f. h The force, with potential energy V h :

[0067]

[0068] Among them, h ik It is the distance between the i-th drone and the k-th virtual leader.

[0069] To achieve asymptotic stability of the swarm system, i.e., all drones move at the desired speed, a controlled dissipative force also needs to be applied. Where K is a positive constant.

[0070] As the drone swarm moves at velocity v0(t) along with the virtual leader, the control force u applied to the i-th drone... i Defined as:

[0071]

[0072] The motion model of an unmanned bee swarm is defined as follows:

[0073]

[0074] Where x is the state variable vector of the unmanned bee colony, u is the external control input, p is the control parameter of the internal control strategy, t is time, and f is the dynamic equation of the bee colony's state variables.

[0075] Step 2: Based on the observable area division of the communication range by the optimal steady-state system, the jammer needs to move at least one drone from inside to outside the communication range of another drone, or from outside to inside, that is, push some drone targets into the "observable area".

[0076] Under optimal steady-state conditions, there exists The fixed structure is defined by d0, where d0 represents the distance between two adjacent drones in the optimal steady-state configuration, which can be deduced from the steady-state formation, while d1 represents the communication distance to be estimated. Therefore, the two interference scenarios described above can be created by appropriately scaling the configuration. The interferer needs to push one of the two drones that were originally d0 apart in the optimal steady-state configuration away to a distance of... Alternatively, the distance between them in the optimal steady state could be greater than or equal to... One of the two drones advanced to within d0 of each other.

[0077] like Figure 1 As shown, under the optimal steady-state structure, for the target drone C at the edge point, other drones can be divided into two categories: "Outside-to-Inside Set" (O-IS) and "Inside-to-Outside Set" (I-OS). In the optimal steady state, target drone C is outside the communication range of drones in the O-IS and within the communication range of drones in the I-OS. Therefore, to ensure that target drone C can cross the communication range of other drones, the interferer should push it to a position no greater than d0 from a drone in the O-IS, or no less than d0 from a drone in the I-OS. The location. Similarly, the jammer uses similar jamming methods on edge point targets UAVs A, B, and D.

[0078] Step 3: Construct the optimal interferator movement planning problem and estimate the communication range of the unmanned swarm. The estimation method for the communication range d1 is as follows:

[0079] Let parameter p be a variable, and generate the corresponding observation trajectory. calculate The deviation from the original trajectory y is minimized to obtain the optimized parameters. Value. The optimization algorithm used here is the biologically inspired evolutionary algorithm CMA-ES; the time interval of the original trajectory y is [t1, t2]. The specific problem description is as follows:

[0080] Solve

[0081] minimize

[0082]

[0083] in, This represents the deviation between two trajectories. The optimization algorithm used here is the biologically inspired evolutionary algorithm CMA-ES.

[0084] Meanwhile, the disruptor needs to satisfy the following assumptions and optimization conditions:

[0085] (1) In order to simplify the intrusion process, the speed of the interferer after takeoff is set to a constant, and there is no need to change the direction or speed during the flight.

[0086] (2) In order to ensure that the interference effect is achieved, the jammer needs to push the target UAV to the "observable area" to achieve at least one observable situation.

[0087] (3) To minimize the degree of interference, the comparison of the position and energy of the drone swarm before and after interference was used as the basis for judgment. Two indicators were used to quantitatively analyze the degree of impact on the drone swarm: position-based indicators and energy-based indicators. Since the virtual leader is the key control point of the swarm, the degree of interference in the position-based indicators was defined as the change in the relative position between each drone and the virtual leader.

[0088]

[0089] Among them I pos This is an indicator of the degree of influence of changes in the position of the bee colony. Dist(i,vir,t) represents the distance between drone i and the virtual leader at time t. T represents the time when the disruptor leaves the drone colony, and t0 represents the time when the disruptor begins to interfere.

[0090] The energy-based index utilizes the Lyapunov function Φ defined in the artificial potential field method, which is the sum of total kinetic energy and artificial potential energy, to quantify the impact:

[0091] I en =Φ(t0+T)-Φ(t0);

[0092]

[0093] Among them I en It is an indicator based on the degree of influence of changes in bee colony energy. v0 is the speed of the virtual leader; m is the total number of virtual leaders.

[0094] Based on the above descriptions and formulas, the optimal interactive control problem can be summarized as minimizing... in Let represent the loss function for interfering with the target drone i, and TS represent the set of target drones that the interferer uses to interfere with the drone swarm. For example... Figure 1 As shown, when the interferer is located in the lower right of the group in the diagram, the four drones labeled "A, B, C, and D" constitute the target area TS, which only includes the system edge nodes that the interferer can directly contact. Loss function The penalty for both performance guarantees and minimal interference was taken into account.

[0095]

[0096] Where k = 1, ..., N represents the discrete observation time index, the observation interval is ΔT, and the total observation time is T = NΔT. The loss value I represents the impact on the unmanned bee colony, which, depending on the selected impact index, can be I0. pos Or I enD(i,T) represents the interference performance guarantee loss. If, at any observation time t, the distance between target UAV i and O-IS (denoted as Dist(i,O-IS,t)) is less than d0, or the distance between target UAV i and I-OS (denoted as Dist(i,I-OS,t)) is greater than d0... If the performance guarantee loss is negative, then the observability requirement has been met, and we only need to consider minimizing the impact on the unmanned bee colony; the total loss is I. If the performance guarantee loss is positive, it means that the observability requirement within the communication range has not yet been met; the total loss is set as the sum of I and D(i,T), with a weighting coefficient of c.

[0097] Step 4: Based on the difference between the estimated and actual communication range of the unmanned swarm, and the loss results obtained from the optimization algorithm, the degree of interference of the interfering party to the unmanned swarm is quantitatively calculated, and the best interfering party speed that can accurately estimate the communication range is selected.

[0098] A second aspect of the present invention: an adversarial unmanned swarm communication range estimation device based on motion behavior, comprising the following modules:

[0099] Establish system modules: Establish an unmanned bee colony system and determine the optimal steady-state system structure;

[0100] Interference module: Based on the optimal steady-state system, edge points are determined, the observable area of ​​the communication range is divided, and interfering agents are introduced at different edge points to interfere with the unmanned bee colony;

[0101] Estimation module: Constructs the optimal motion planning problem for the interfering party and estimates the communication range of the unmanned swarm;

[0102] Screening module: Compare the difference between the estimated and actual communication range of the unmanned swarm, and select the optimal speed of the interferer based on the degree of interference of the interferer to the unmanned swarm.

[0103] This invention analyzes the motion behavior of an adversarial swarm under optimal steady-state conditions using an intrusion-based interactive method. An optimization framework is established to calculate the optimal intruder trajectory, thereby estimating the true communication range. Simultaneously, the impact of the intruder is minimized by analyzing the changes in the swarm's position and energy before and after the intrusion. Simulation results demonstrate the effectiveness and advantages of this motion behavior-based adversarial swarm communication range estimation method.

[0104] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0105] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for estimating the communication range of adversarial unmanned swarms based on motion behavior, characterized in that, Includes the following steps: (1) Establish an unmanned swarm system and determine the optimal steady-state system structure. The unmanned swarm system is based on an artificial potential field model and consists of n unmanned aerial vehicles (UAVs) and m virtual leaders. Each UAV in the system is affected by three control forces, namely the interaction force between adjacent UAVs, the force exerted by the virtual leader on the UAV, and the controlled dissipation force. (2) Determine the edge points based on the optimal steady-state system, divide the observable area of ​​the communication range, and introduce interferers to interfere with the unmanned bee colony at different edge points; (3) Construct the optimal interferer motion planning problem and estimate the communication range of the drone swarm; only the true value can trigger the dynamics of the observation group. The interferer needs to move at least one drone from inside to outside the communication range of another drone, or from outside to inside, that is, push some drone targets into the "observable area"; the estimation method of communication distance d1 is as follows: Let parameter p be a variable, and generate the corresponding observation trajectory. calculate The deviation from the original trajectory y is minimized to obtain the optimized parameters. The value, the time interval [t1, t2] of the original trajectory y, is as follows: Solve Minimize in, This represents the deviation between two trajectories. The optimization algorithm used here is the biologically inspired evolutionary algorithm CMA-ES. (4) Compare the difference between the estimated and actual range of the unmanned swarm communication range, and select the optimal speed of the interfering agent based on the degree of interference of the interfering agent on the unmanned swarm.

2. The method for estimating the range of adversarial unmanned swarm communication based on motion behavior according to claim 1, characterized in that, The interaction force between adjacent UAVs is used to force the required inter-UAV spacing; the interaction force f I The magnitude and corresponding potential energy V I All depend on the distance x between drone i and its neighboring drone j. ij It is expressed by the following formula: Where α is the scalar control gain, and d0 and d1 are scalar constants; since the interaction force f only occurs when the distance is less than d1. I Since it is not zero, in the actual implementation of the artificial potential field model, d1 is usually equal to the communication range; The virtual leader of the unmanned swarm will also exert an effect of size f. h The force, with potential energy V h The expression is as follows: Among them, h ik It is the distance between the i-th drone and the k-th virtual leader; To ensure the stability of the swarm system, i.e., that all drones move at the desired speed, a controlled dissipative force also needs to be applied. Where K is a positive constant; As the drone swarm moves at velocity v0(t) along with the virtual leader, the control force u applied to the i-th drone... i Defined as: Its motion model for unmanned bee swarms is defined as: Where x is the state variable vector of the unmanned bee colony, u is the external control input, p is the control parameter of the internal control strategy, t is time, and f is the dynamic equation of the bee colony's state variables.

3. The method for estimating the range of adversarial unmanned swarm communication based on motion behavior according to claim 2, characterized in that, When the unmanned swarm reaches a steady state, the swarm members maintain the same speed and the relative distance remains unchanged, meaning that the control force of each drone is zero; the system steady state includes the following two types: (a) Normal steady state: Under normal steady state, the attractive and repulsive forces of each drone are in equilibrium, and the total control force is zero; (b) Optimal steady state: In the optimal steady state, the force between any two UAVs is zero, that is, there is no attractive or repulsive force between any UAVs. Normal steady state may have various cluster formations, but the optimal steady state always has a fixed cluster formation structure, that is, the distance between UAVs is a fixed value d0, which makes it easy for the interferer to interfere with the edge points of the optimal steady state system with a fixed structure.

4. The method for estimating the range of adversarial unmanned swarm communication based on motion behavior according to claim 1, characterized in that, The observable range of unmanned swarm communication is defined as follows: Only true values ​​can trigger the observation of group dynamics. The interferer needs to move at least one drone from inside the communication range of another drone to the outside, or from the outside to the inside, that is, push some drone targets into the "observable area". Under optimal steady-state conditions, the distance between two adjacent UAVs is a fixed value d0. The value of d0 can be inferred from the steady-state formation, and there exists... The structure is given by d1, where d1 is the communication distance to be estimated. Therefore, the two interference scenarios mentioned above can be created by appropriately scaling the scale. The interferer needs to push one of the two drones that were originally d0 apart in the optimal steady state to a distance of d0. Alternatively, the distance between them in the optimal steady state could be greater than or equal to... One of the two drones advanced to within d0 of each other.

5. The method for estimating the range of adversarial unmanned swarm communication based on motion behavior according to claim 4, characterized in that, The interfering party needs to satisfy the following assumptions and optimization conditions: (a) Set the speed of the interfering party after takeoff to a constant, thereby simplifying the intrusion process, and there is no need to change direction or speed during flight; (b) In order to ensure that the communication range is observable, the jammer needs to move the target UAV to the "observable area" to achieve at least one observable condition; (c) To minimize the degree of interference, the comparison of the position and energy of the drone swarm before and after the interference should be used as the basis for judgment; two indicators are used to quantitatively analyze the degree of impact on the drone swarm: position-based indicators and energy-based indicators; since the virtual leader is the key control point of the swarm, the degree of interference in the position-based indicators is defined as the change in the relative position between each drone and the virtual leader, as follows: Where I pos It is an indicator based on the degree of influence of changes in the position of the bee colony. Dist(i,vir,t) represents the distance between drone i and the virtual leader at time t, n is the number of drones in the drone colony, T represents the time when the interferer leaves the drone colony, and t0 represents the time when the interferer begins to interfere. The energy-based index utilizes the Lyapunov function Φ defined in the artificial potential field method, which is the sum of total kinetic energy and artificial potential energy, to quantify the influence, as defined below: I en =Φ(t0+T)-Φ(t0); Where I en It is an index based on the degree of influence of changes in bee colony energy, where k = 1, ..., N represents the discrete observation time index; Let m be the speed of the virtual leader, and m be the total number of virtual leaders. Based on the above descriptions and formulas, the optimal interactive control problem can be summarized as minimizing... in Let represent the loss function of the target drone i, and let TS represent the set of target drones that the interferer uses to interfere with the drone swarm.

6. An apparatus for estimating the range of adversarial unmanned swarm communication based on motion behavior as described in any one of claims 1-5, characterized in that, Includes the following modules: Establish system modules: Establish an unmanned bee colony system and determine the optimal steady-state system structure; Interference module: Based on the optimal steady-state system, edge points are determined, the observable area of ​​the communication range is divided, and interfering agents are introduced at different edge points to interfere with the unmanned bee colony; Estimation module: Constructs the optimal motion planning problem for the interfering party and estimates the communication range of the unmanned swarm; Screening module: Compare the difference between the estimated and actual communication range of the unmanned swarm, and select the optimal speed of the interferer based on the degree of interference of the interferer to the unmanned swarm.

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