Resistance unmanned bee colony communication range estimation method based on motion behaviors

Abstract

The invention discloses a countering unmanned bee colony communication range estimation method based on motion behaviors. And the antagonistic unmanned swarms are analyzed through observation and interaction, so that the observability of the communication range of the unmanned aerial vehicle is ensured. On the basis of a jammer intrusion method, a numerical calculation framework for generating a jammer trajectory is designed to push some unmanned aerial vehicle targets to an observable area, meanwhile, the position and energy change of an unmanned bee colony before and after interference are considered, and the influence of the jammer on the unmanned bee colony is reduced to the maximum extent. According to the method, the possible unobservability in the communication range of the unmanned bee colony is solved, and the counterpart unmanned bee colony is cognized by using the dynamic behavior of the unmanned bee colony under the condition of lacking priori knowledge.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned systems, and in particular relates to a method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior. Background Art

[0002] With the growth of autonomous drone swarm applications, the importance of analyzing swarm behavior has become increasingly prominent. Typical goals include estimating communication topology, computing inter-drone cooperation strategies, inferring swarm structure, or 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 neighbor drones received within the communication range. Therefore, correctly estimating the communication range of adversarial drone swarms provides key insights into swarm cognition.

[0003] Research related to understanding the characteristics of UAV swarm communication networks mainly focuses on topological reasoning, which can be divided into two categories: cooperative reasoning and non-cooperative reasoning. Cooperative topological reasoning can access the UAV swarm network, and the monitoring system can exchange data with the network, and the node connection graph of the measured network can be calculated. However, due to unknown message encryption or authentication mechanisms, the wireless network of adversarial UAV swarms is usually not easy to access, so non-cooperative topological reasoning is the main method of analysis. In non-cooperative topological reasoning, 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 communication behavior observation, that is, monitoring the communication signals between drones, and then constructing the corresponding signal feature sequence to infer the communication relationship 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 the precise communication range value is different from topological reasoning, because once the communication range of each drone is known, the group communication topology can be inferred based on the observed drone position; but the opposite is not true, and the communication range cannot be inferred from the communication topology. Second, when the communication range of each drone is small and the sensors are located outside the communication range, the data exchange within the drone swarm cannot be measured. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method for estimating the communication range of unmanned bee colonies based on motion behavior, which analyzes the adversarial unmanned bee colonies through observation and interaction to ensure the observability of the communication range of unmanned aerial vehicles. Based on the method of interferer intrusion, a numerical calculation framework for generating interferer trajectories is designed to push some unmanned aerial vehicle targets into the "observable area", while taking into account the motion targets and the impact on the enemy group, minimizing the impact on the adversarial unmanned bee colony.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: a method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior, comprising the following steps:

[0006] (1) Establish an unmanned swarm 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 jammer motion planning problem and estimate the communication range of the unmanned swarm;

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

[0010] Furthermore, the unmanned swarm system in step (1) is established based on an artificial potential field model, and is composed of n unmanned aerial vehicles and m virtual leaders; each unmanned aerial vehicle in the system is affected by three control forces, namely, the interaction force between adjacent unmanned aerial vehicles, the force of the virtual leader on the unmanned aerial vehicle, and the controlled dissipative force.

[0011] Furthermore, the interaction force between adjacent UAVs is used to enforce the required distance between them; the interaction force f I The size and corresponding potential energy V I All depend on the distance x between UAV i and its neighboring UAV j ij , expressed by the following formula:

[0012]

[0013] where α is the scalar control gain, d0 and d1 are scalar constants; since the interaction force f I It is not zero, so 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 bee colony also imposes a h The force, potential energy is V h , the expression is as follows:

[0015]

[0016] Among them, h ik is the distance between the ith UAV and the kth virtual leader;

[0017] In order to make the swarm system stable, that is, all drones move at the desired speed, a controlled dissipative force needs to be applied Where K is a positive constant;

[0018] When the drone swarm moves with the virtual leader at speed v0(t), the control force u exerted on the i-th drone is i Defined as:

[0019]

[0020]

[0021] The motion model of its unmanned swarm 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 the time, and f is the dynamic equation of the bee colony state variable.

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

[0025] (a) Normal steady state: In the normal steady state, the attractive and repulsive forces of each UAV are balanced, and the total control force is zero;

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

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

[0028] Only true values ​​can trigger the observation of group dynamics. 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”;

[0029] Under the optimal steady-state condition, the distance between two adjacent drones is a fixed value d0, which can be inferred from the steady-state formation, and there is structure, and d1 is the communication distance to be estimated; therefore, the above two interference situations can be formed by appropriate scaling. The jammer needs to push one of the two drones that were originally separated by d0 in the optimal steady state to a distance of In addition, or the distance between the original optimal steady state is greater than or equal to One of the two drones advances to within a distance of d0.

[0030] Furthermore, the communication distance d1 is estimated 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 Value, the time interval of the original trajectory y is [t1, t2], specifically:

[0032] Solution

[0033] minimize

[0034]

[0035] in, Represents the deviation between two trajectories. The optimization algorithm here adopts the biologically inspired evolutionary algorithm CMA-ES.

[0036] Furthermore, the interferer needs to meet the following assumptions and optimization conditions:

[0037] (a) The jammer's speed after takeoff is set to a constant, thereby simplifying the intrusion process and eliminating the need to change direction or speed during flight;

[0038] (b) In order to ensure that the communication range is observable, the jammer needs to push the target drone to the "observable area" to achieve at least one observable situation;

[0039] (c) To minimize the degree of interference, the position and energy comparison of the drone swarm before and after the interference is used as a 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, which is defined as follows:

[0040]

[0041] Among them I pos It is an impact index based on the change of the swarm position. 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 swarm, T represents the time when the interferer leaves the swarm, and t0 represents the time when the interferer starts to interfere.

[0042] Energy-based indicators use the Lyapunov function Φ defined in the artificial potential field method, which is the total kinetic energy plus the artificial potential energy, to quantify the impact and is defined as follows:

[0043] I en =Φ(t0+T)-Φ(t0);

[0044]

[0045] Among them I en It is an index based on the influence degree of the change of the energy of the swarm, k = 1, ..., N represents the discrete observation time index; v0 is the velocity of the virtual leader, m is the total number of virtual leaders;

[0046] Combining the above description and formula, the optimal interactive control problem formula is to minimize in represents the loss function of interfering with the target UAV i, and TS represents the set of target UAVs that the jammer interferes with the UAV swarm.

[0047] The present invention also provides a device for estimating the communication range of an adversarial unmanned bee colony based on motion behavior, comprising the following modules:

[0048] Establish system modules: establish unmanned swarm system and determine the optimal steady-state system structure;

[0049] Interference module: 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;

[0050] Estimation module: construct the optimal jammer motion planning problem and estimate the communication range of the unmanned swarm;

[0051] Screening module: Compare the difference between the estimated value and the actual value of the communication range of the unmanned bee colony, and screen the optimal jammer speed based on the degree of interference of the jammer on the unmanned bee colony.

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

[0053] The present invention proposes a method for unmanned bee swarm motion path planning based on attention allocation. Aiming at the unmanned bee swarm model based on the artificial potential field method, a comprehensive analysis is conducted on the estimation method of the motion behavior of unmanned aerial vehicles that only relies on position information, so as to accurately estimate the communication range of unmanned aerial vehicles in the adversarial unmanned aerial vehicle swarm. Dividing the observable area of ​​the communication range can ensure the observability of the communication range of the unmanned aerial vehicle, and establishing a quantitative calculation framework based on the interference method can minimize the impact on the adversarial unmanned bee swarm while ensuring the observability of the communication range of the unmanned aerial vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0055] Figure 1 It is the observable area division diagram of the communication range of the unmanned bee colony under the optimal steady state;

[0056] Figure 2 It is a flow chart of the communication range estimation method of adversarial unmanned swarm based on motion behavior. DETAILED DESCRIPTION

[0057] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0058] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0059] The following is combined with Figure 1 and attached Figure 2 The present invention is further explained with specific embodiments:

[0060] The first aspect of the present invention: The method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior of the present invention comprises the following steps:

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

[0062] One is the interaction force between adjacent drones, which is used to enforce the required spacing between drones. I The size and corresponding potential energy V I All depend on the distance x between UAV i and its neighboring UAV j ij , which are expressed by the following formula:

[0063]

[0064]

[0065] where α is the scalar control gain, and d0 and d1 are scalar constants. Since the interaction force f I It is not zero, so in the actual implementation of the artificial potential field model, d1 is usually equal to the communication range.

[0066] Similarly, the virtual leader near the drone will also impose a magnitude of f h The force, potential energy is V h :

[0067]

[0068] Among them, h ik is the distance between the ith UAV and the kth virtual leader.

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

[0070] When the drone swarm moves with the virtual leader at speed v0(t), the control force u exerted on the i-th drone is i Defined as:

[0071]

[0072] The motion model of the unmanned swarm is defined as:

[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 the time, and f is the dynamic equation of the bee colony state variable.

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

[0076] Under optimal steady-state conditions, there is The fixed structure of d0 is the distance between two adjacent drones in the optimal steady-state structure, which can be inferred from the steady-state formation, and d1 is the communication distance to be estimated. Therefore, the above two interference situations can be formed by appropriate scaling. The jammer needs to push one of the two drones that were originally d0 apart in the optimal steady-state to a distance of In addition, or the distance between the original optimal steady state is greater than or equal to One of the two drones advances to within a distance of d0.

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

[0078] Step 3: Construct the optimal jammer motion 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 The optimization algorithm here uses the biologically inspired evolutionary algorithm CMA-ES; the time interval of the original trajectory y is [t1, t2], and the specific problem is described as follows:

[0080] Solution

[0081] minimize

[0082]

[0083] in, Represents the deviation between two trajectories. The optimization algorithm here adopts the biologically inspired evolutionary algorithm CMA-ES.

[0084] At the same time, the disruptor needs to meet the following assumptions and optimization conditions:

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

[0086] (2) In order to ensure that the jammer can achieve the desired jamming effect, the jammer needs to push the target UAV into the “observable area” and achieve at least one observable situation.

[0087] (3) In order to minimize the degree of interference, the position and energy comparison of the drone swarm before and after the interference is 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.

[0088]

[0089] Among them I pos It is an impact index based on the change of the swarm position. Dist(i,vir,t) represents the distance between drone i and the virtual leader at time t. T represents the time when the jammer leaves the swarm, and t0 represents the time when the jammer starts to interfere.

[0090] Energy-based indicators use the Lyapunov function Φ defined in the artificial potential field method, which is the total kinetic energy plus the 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 impact of changes in the energy of the bee colony. v0 is the velocity of the virtual leader; m is the total number of virtual leaders.

[0094] Combining the above description and formula, the optimal interactive control problem formula is to minimize in represents the loss function of interfering with the target drone i, and TS represents the set of target drones that the jammer interferes with the drone swarm. Figure 1 As shown in Figure 1, when the interferer is located at the lower right of the group in the figure, the four drones marked as "A, B, C, D" constitute the target area TS, which only contains the system edge nodes that the interferer can directly contact. Loss function The performance guarantee and the penalty for minimal interference are considered jointly:

[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. Depending on the selected impact index, it can be I pos or I enD(i,T) represents the interference performance guarantee loss. If at any observation time t, the distance between the target UAV i and O-IS (denoted as Dist(i,O-IS,t)) is less than d0, or the distance between the target UAV i and I-OS (denoted as Dist(i,I-OS,t)) is greater than The performance guarantee loss is negative, and the observability requirement is met. We only need to consider the minimum impact on the unmanned swarm, and the total loss is I. If the performance guarantee loss value is positive, it means that the observability requirement of the communication range has not been met. The total loss is set to the sum of I and D(i,T), and the weight coefficient is c.

[0097] Step 4: Based on the difference between the estimated and true values ​​of the communication range of the unmanned bee colony, and the loss results obtained by the optimization algorithm, the degree of interference of the interferer on the unmanned bee colony is quantitatively calculated, and the optimal interferer speed that can accurately estimate the communication range is selected.

[0098] The second aspect of the present invention is a device for estimating the communication range of an adversarial unmanned bee colony based on motion behavior, comprising the following modules:

[0099] Establish system modules: establish unmanned swarm system and determine the optimal steady-state system structure;

[0100] Interference module: 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;

[0101] Estimation module: construct the optimal jammer motion planning problem and estimate the communication range of the unmanned swarm;

[0102] Screening module: Compare the difference between the estimated value and the actual value of the communication range of the unmanned bee colony, and screen the optimal jammer speed based on the degree of interference of the jammer on the unmanned bee colony.

[0103] The present invention uses an interactive method of invasion to analyze the motion behavior of the unmanned bee colony in the optimal steady state, establishes an optimization framework to calculate the optimal intruder trajectory, and estimates the true communication range value. At the same time, the influence of the interferer is minimized from the position and energy changes of the unmanned bee colony before and after the interference. The simulation experimental results prove the effectiveness and advantages of the communication range estimation method of the adversarial unmanned bee colony based on motion behavior.

[0104] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0105] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior, characterized in that: The following steps are involved: (1) Establish an unmanned swarm system and determine the optimal steady-state system structure; (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 jammer motion planning problem and estimate the communication range of the unmanned swarm; (4) Compare the difference between the estimated and actual communication range of the unmanned bee colony, and select the optimal jammer speed based on the degree of interference of the jammer on the unmanned bee colony.

2. The method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior according to claim 1, characterized in that: The unmanned swarm system in step (1) is established based on an artificial potential field model and consists of n unmanned aerial vehicles and m virtual leaders; each unmanned aerial vehicle in the system is affected by three control forces, namely, the interaction force between adjacent unmanned aerial vehicles, the force of the virtual leader on the unmanned aerial vehicle, and the controlled dissipative force.

3. The method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior according to claim 2, characterized in that: The interaction force between adjacent UAVs is used to enforce the required distance between them; the interaction force f I The size and corresponding potential energy V I All depend on the distance x between UAV i and its neighboring UAV j ij , expressed by the following formula: where α is the scalar control gain, d0 and d1 are scalar constants; since the interaction force f I It is not zero, so in the actual implementation of the artificial potential field model, d1 is usually equal to the communication range; The virtual leader of the unmanned bee colony also imposes a h The force, potential energy is V h , the expression is as follows: Among them, h ik is the distance between the ith UAV and the kth virtual leader; In order to make the swarm system stable, that is, all drones move at the desired speed, a controlled dissipative force needs to be applied Where K is a positive constant; When the drone swarm moves with the virtual leader at speed v0(t), the control force u exerted on the i-th drone is i Defined as: The motion model of its unmanned swarm 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 the time, and f is the dynamic equation of the bee colony state variable.

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

5. The method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior according to claim 1, characterized in that: The observable situation of the unmanned bee colony communication range in step (3) is defined as: Only true values ​​can trigger the observation of group dynamics. 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"; Under the optimal steady-state condition, the distance between two adjacent drones is a fixed value d0, which can be inferred from the steady-state formation, and there is structure, and d1 is the communication distance to be estimated; therefore, the above two interference situations can be formed by appropriate scaling. The jammer needs to push one of the two drones that were originally separated by d0 in the optimal steady state to a distance of In addition, or the distance between the original optimal steady state is greater than or equal to One of the two drones advances to within a distance of d0.

6. The method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior according to claim 5, characterized in that: The estimation method of the 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 Value, the time interval of the original trajectory y is [t1, t2], specifically: Solution Minimize s.t. in, Represents the deviation between two trajectories. The optimization algorithm here adopts the biologically inspired evolutionary algorithm CMA-ES.

7. The method for estimating the communication range of an adversarial unmanned bee colony based on motion behavior according to claim 5, characterized in that: The disruptor needs to meet the following assumptions and optimization conditions: (a) The jammer's speed after takeoff is set to a constant, thereby simplifying the intrusion process and eliminating the need to change direction or speed during flight; (b) In order to ensure that the communication range is observable, the jammer needs to push the target drone to the "observable area" to achieve at least one observable situation; (c) To minimize the degree of interference, the position and energy comparison of the drone swarm before and after the interference is used as a 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, which is defined as follows: Among them I pos It is an impact index based on the change of the swarm position. 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 swarm, T represents the time when the interferer leaves the swarm, and t0 represents the time when the interferer starts to interfere. Energy-based indicators use the Lyapunov function Φ defined in the artificial potential field method, which is the total kinetic energy plus the artificial potential energy, to quantify the impact and is defined as follows: I en =Φ(t0+T)-Φ(t0); Among them I en It is an index based on the influence degree of the change of the energy of the swarm, k = 1, ..., N represents the discrete observation time index; v0 is the velocity of the virtual leader, m is the total number of virtual leaders; Combining the above description and formula, the optimal interactive control problem formula is to minimize in represents the loss function of interfering with the target UAV i, and TS represents the set of target UAVs that the jammer interferes with the UAV swarm.

8. A device for estimating the communication range of an adversarial unmanned bee colony based on motion behavior, characterized in that: Includes the following modules: Establish system modules: establish unmanned swarm system and determine the optimal steady-state system structure; Interference module: 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; Estimation module: construct the optimal jammer motion planning problem and estimate the communication range of the unmanned swarm; Screening module: Compare the difference between the estimated value and the actual value of the communication range of the unmanned bee colony, and screen the optimal jammer speed based on the degree of interference of the jammer on the unmanned bee colony.

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