Satellite navigation deception method and device for swarming controlled drone clusters

Abstract

The present application relates to a satellite navigation deception method and device for swarm-controlled drone clusters. The method includes: constructing a dynamic model of a swarm-controlled drone cluster; using the dynamic model to derive a functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, and calculating the distance by which the entire drone cluster is deceived; establishing an objective function of the satellite navigation deception model based on the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, and setting constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance, and the distance by which the entire drone cluster is deceived; solving the satellite navigation deception model to obtain a deception offset; and using the deception offset to generate a satellite navigation deception signal to deceive and control the swarm-controlled drone cluster. This method can effectively control and counter swarm-controlled drone clusters.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation deception technology, and in particular to a satellite navigation deception method for swarm-controlled drone clusters. Background Art

[0002] Satellite navigation spoofing, as a mainstream counter-drone method, offers unique technical advantages in the control and countermeasures of drone swarms. However, with the continuous deepening of research on the behavior of biological swarms in nature, researchers have extracted rules such as separation, aggregation, and velocity alignment from the intelligent swarming behavior of biological populations. They have developed a flocking control algorithm and applied it to the autonomous control of unmanned swarms, which has gradually enabled unmanned swarms to exhibit certain intelligent characteristics such as self-organization, adaptability, scalability, and robust self-healing. Using existing deception methods to spoof the navigation of flocking-controlled drone swarms will result in the deception failing because individual swarm members cannot simultaneously achieve velocity alignment and extreme value constraints, making it difficult to effectively control and counter the flocking-controlled drone swarms. Summary of the Invention

[0003] Based on this, it is necessary to provide a satellite navigation deception method and device for swarming controlled drone clusters that can effectively manage and counter swarming controlled drone clusters to address the above technical problems.

[0004] A satellite navigation deception method for swarming controlled drone clusters, the method comprising:

[0005] Construct a dynamic model for swarming drone control. Use the dynamic model to derive the functional relationship between the satellite navigation spoofing offset and the position and velocity of the drone cluster, and calculate the distance the entire drone cluster is deceived.

[0006] Establish an objective function of the satellite navigation deception model based on the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster; set constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance, and the distance of the drone cluster as a whole being deceived; establish a satellite navigation deception model based on the objective function and the constraints;

[0007] The satellite navigation spoofing model was solved to obtain the spoofing offset. The spoofing offset was used to generate a satellite navigation spoofing signal to deceive the swarming control drone cluster, successfully deceiving some drones in the cluster.

[0008] Under the influence of the three principles of swarming control algorithm, the speed of some individual drones in the swarm is deceived to drive the change of the speed of the entire drone swarm. During the deception process, the direction of applying the deception offset is adjusted to prevent the speed and spacing of the individual drones in the swarm from exceeding the extreme values, thereby achieving deception of the entire drone swarm.

[0009] In one embodiment, constructing a dynamic model for swarming control of a drone swarm includes:

[0010] The dynamic model of swarming control UAV cluster is constructed as

[0011]

[0012] Where α and β are constant coefficients, L is the Laplace matrix of the UAV cluster, I is the identity matrix, and: x=[P T V T a T ] T , a i are the acceleration of cluster individual i, p i ,v i ,u i are the position, velocity and control input of cluster individual i respectively.

[0013] In one embodiment, a dynamic model is used to derive a functional relationship between a satellite navigation spoofing offset and the position and velocity of a drone cluster, including:

[0014] The dynamic model is used to derive the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster. The position of the cluster after the satellite signal is deceived is obtained as follows:

[0015]

[0016] Where, q=[q1 q1…q n ] T is the eigenvector corresponding to the zero eigenvalue of the Laplace matrix of the drone cluster, δp i ,δv i are the deceptive offsets of position and velocity caused by navigation deception in real satellite signals, dT is the interval between two adjacent moments, Δv i is the velocity change between two adjacent moments, t represents the moment, p i (t) represents the location information of the i-th UAV at time t, v i (t) represents the speed information of the i-th UAV at time t, and n represents the number of UAVs in the UAV cluster.

[0017] In one embodiment, the dynamic model is used to derive the functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, and the speed of the cluster after the satellite signal is spoofed is obtained as follows:

[0018]

[0019] In one embodiment, the distance that the entire drone cluster is deceived is calculated as

[0020]

[0021] Where, q=[q1 q1…q n ] T is the eigenvector corresponding to the zero eigenvalue of the Laplace matrix of the drone cluster, δp i ,δv i are the deceptive offsets of position and velocity caused by navigation deception in real satellite signals, dT is the interval between two adjacent moments, Δv i is the speed change between two adjacent moments, t represents the moment, and n represents the number of drones in the drone cluster.

[0022] In one embodiment, a satellite navigation deception model is established based on an objective function and constraints, including:

[0023] According to the objective function and constraints, the satellite navigation deception model is established as follows:

[0024]

[0025] Constraints:

[0026]

[0027] δr c ≥δd ref

[0028] r safe ≤d ij ≤r ref

[0029] t s =t e

[0030] Φ u ≥K u

[0031] in, is the speed value of cluster individual i after being deceived, δv max is the maximum value of the speed deception offset, v max is the maximum speed that individual i in the UAV cluster can reach, rsafe is the safe distance radius between cluster individuals, r ref is the effective communication distance radius between cluster individuals, Φ u is the overall coefficient of the cluster, K u is a constant coefficient, t e is the expected cheating time, t s is the actual cheating time, δr c Indicates the distance that the entire drone cluster is deceived, δd ref represents the expected deception distance, v i represents the speed of individual i in the drone cluster, δv i (t) represents the deceptive offset of the speed due to navigation deception in the real satellite signal, and n represents the number of drones in the drone cluster.

[0032] A satellite navigation deception device for swarming controlled drone clusters, the device comprising:

[0033] The spoofing offset derivation module is used to build a dynamic model for swarming drone control. This model is used to derive the functional relationship between the satellite navigation spoofing offset and the position and velocity of the drone cluster, and to calculate the distance that the entire drone cluster has been deceived.

[0034] Establish a satellite navigation deception model module, which is used to establish the objective function of the satellite navigation deception model based on the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, and set the constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance, and the distance of the drone cluster as a whole being deceived; establish the satellite navigation deception model based on the objective function and the constraints;

[0035] The satellite navigation deception model solving module is used to solve the satellite navigation deception model to obtain the deception offset. The deception offset is used to generate a satellite navigation deception signal to deceive the swarming control drone cluster, successfully deceiving some drones in the cluster.

[0036] The drone swarm overall deception module is used to deceive the speed of some individual drones in the swarm under the action of the three principles of the swarm control algorithm to drive the change of the speed of the entire drone swarm. During the deception process, the direction of applying the deception offset is adjusted to prevent the speed and spacing of individual drones in the swarm from exceeding the extreme values, thereby achieving deception of the entire drone swarm.

[0037] The above-mentioned satellite navigation deception method and device for swarming controlled drone clusters, the present invention first constructs a dynamic model of the swarming controlled drone cluster, derives the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, then calculates the distance of the entire drone cluster being deceived, establishes the objective function of the satellite navigation deception model based on the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, sets the constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance and the distance of the entire drone cluster being deceived; establishes a satellite navigation deception model based on the objective function and the constraints, solves the satellite navigation deception model to obtain the distance that can keep the drones The deception offset of cluster separation, aggregation and alignment behavior is used, and then the satellite navigation deception signal is generated by the deception offset to deceive the swarm-controlled drone cluster, successfully deceiving some drones in the cluster. Finally, under the action of the three principles of the swarm control algorithm, the speed of some cluster individuals is deceived to drive the change of the speed of the entire drone cluster. At the same time, during the deception process, the direction of application of the deception offset is adjusted to avoid the speed and spacing of the cluster individuals exceeding the extreme value, thereby achieving deception of the entire cluster. This application can deceive the swarm-controlled drone cluster to a pre-set target position, improve the deception effect on the entire drone cluster, and can be applied to various scenarios for controlling and countering illegal drone clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 1 is a flow chart of a method for swarming controlled drone swarms in accordance with an embodiment of the present invention;

[0039] Figure 2 1 is a structural block diagram of a satellite navigation spoofing device for swarm-controlled drone clusters in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0041] In one embodiment, Figure 2 As shown, a satellite navigation deception method for swarming control of drone clusters is provided, comprising the following steps:

[0042] Step 102: Construct a dynamic model of a swarming controlled drone cluster. Use the dynamic model to derive a functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, and calculate the distance by which the entire drone cluster is deceived.

[0043] The dynamic model of flocking control of UAV cluster is constructed as

[0044]

[0045] Among them, α, β are constant coefficients, L is the Laplace matrix of the drone cluster, and I is the identity matrix. And:

[0046] x=[P T V T a T ] T ,

[0047] a i are the acceleration of cluster individual i, p i ,v i ,u i are the position, velocity and control input of cluster individual i respectively. The relationship among the position, velocity, acceleration and control input of cluster individual is as follows:

[0048]

[0049] The control input of cluster individual i is

[0050]

[0051] The speed of cluster individual i is

[0052]

[0053] in, u max is the maximum value of the control input, v max is the maximum value of velocity, v flock is the self-propulsion term in the cluster velocity, is the velocity repulsion or attraction term generated by the influence of cluster individual i on cluster individual j, is the velocity alignment term generated between cluster individuals i and cluster individuals j, dT is the time interval between two adjacent moments, and k is the discrete moment.

[0054] The dynamic model is used to derive the functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, including:

[0055] The location of the cluster after the satellite signal is spoofed is

[0056]

[0057] The center of the UAV cluster flight trajectory is

[0058]

[0059] Then the distance that the drone cluster is deceived as a whole is

[0060]

[0061] The speed of the cluster after the satellite signal is spoofed is

[0062]

[0063] Where, q=[q1 q1…q n ] T is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the deception offsets caused by navigation deception in real satellite signals, Δv i is the velocity change between two adjacent moments.

[0064] Step 104: Establish an objective function of the satellite navigation deception model based on the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, set the constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance, and the distance at which the drone cluster is deceived as a whole; and establish the satellite navigation deception model based on the objective function and the constraints.

[0065] According to the objective function and constraints, the satellite navigation deception model is established as follows:

[0066]

[0067] Constraints:

[0068]

[0069] δr c ≥δd ref

[0070] r safe ≤d ij ≤r ref

[0071] t s =t e

[0072] Φ u ≥K u

[0073] in, is the speed value of cluster individual i after being deceived, δv max is the maximum value of the speed deception offset, v max is the maximum speed that cluster individual i can reach, r safeis the safe distance radius between cluster individuals, r ref is the effective communication distance radius between cluster individuals, δd ref represents the expected deception distance, Φ u is the overall coefficient of the cluster, K u is a constant coefficient, t e is the expected cheating time, t s For actual cheating time.

[0074] Step 106: Solve the satellite navigation deception model to obtain a deception offset; use the deception offset to generate a satellite navigation deception signal to deceive the swarming control drone cluster, and successfully deceive some drones in the cluster.

[0075] Step 108, under the influence of the three principles of the swarming control algorithm, the speed of some individual drone clusters is deceived to drive the change of the speed of the entire drone cluster. During the deception process, the direction of applying the deception offset is adjusted to prevent the speed and spacing of the individual drone clusters from exceeding the extreme values, thereby achieving deception of the entire drone cluster.

[0076] The above-mentioned satellite navigation deception method for swarming controlled drone clusters, the present invention first constructs a dynamic model of the swarming controlled drone cluster, derives the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, then calculates the distance of the entire drone cluster being deceived, establishes the objective function of the satellite navigation deception model based on the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, sets the constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance and the distance of the entire drone cluster being deceived; establishes a satellite navigation deception model based on the objective function and the constraints, solves the satellite navigation deception model to obtain a method that can maintain the drone cluster The deception offset of group separation, aggregation and alignment behavior is used, and then the deception offset is used to generate a satellite navigation deception signal to deceive the swarm-controlled drone cluster, successfully deceiving some drones in the cluster. Finally, under the action of the three principles of the swarm control algorithm, the speed of some cluster individuals is deceived to drive the change of the speed of the entire drone cluster. At the same time, during the deception process, the direction of application of the deception offset is adjusted to avoid the speed and spacing of the cluster individuals exceeding the extreme value, thereby achieving deception of the entire cluster. This application can deceive the swarm-controlled drone cluster to a pre-set target position, improve the deception effect on the entire drone cluster, and can be applied to various scenarios for controlling and countering illegal drone clusters.

[0077] In one embodiment, constructing a dynamic model for swarming control of a drone swarm includes:

[0078] The dynamic model of swarming control UAV cluster is constructed as

[0079]

[0080] Where α and β are constant coefficients, L is the Laplace matrix of the UAV cluster, I is the identity matrix, and: x=[P T V T a T ] T , a i are the acceleration of cluster individual i, p i ,v i ,u i are the position, velocity and control input of cluster individual i respectively.

[0081] In one embodiment, a dynamic model is used to derive a functional relationship between a satellite navigation spoofing offset and the position and velocity of a drone cluster, including:

[0082] The dynamic model is used to derive the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster. The position of the cluster after the satellite signal is deceived is obtained as follows:

[0083]

[0084] Where, q=[q1 q1…q n ] T is the eigenvector corresponding to the zero eigenvalue of the Laplace matrix of the drone cluster, δp i ,δv i are the deceptive offsets of position and velocity caused by navigation deception in real satellite signals, dT is the interval between two adjacent moments, Δv i is the velocity change between two adjacent moments, t represents the moment, p i (t) represents the location information of the i-th UAV at time t, v i (t) represents the speed information of the i-th UAV at time t, and n represents the number of UAVs in the UAV cluster.

[0085] In one embodiment, the dynamic model is used to derive the functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, and the speed of the cluster after the satellite signal is spoofed is obtained as follows:

[0086]

[0087] In one embodiment, the distance that the entire drone cluster is deceived is calculated as

[0088]

[0089] Where, q=[q1 q1…q n ] T is the eigenvector corresponding to the zero eigenvalue of the Laplace matrix of the drone cluster, δp i ,δv i are the deceptive offsets of position and velocity caused by navigation deception in real satellite signals, dT is the interval between two adjacent moments, Δv i is the speed change between two adjacent moments, t represents the moment, and n represents the number of drones in the drone cluster.

[0090] In one embodiment, a satellite navigation deception model is established based on an objective function and constraints, including:

[0091] According to the objective function and constraints, the satellite navigation deception model is established as follows:

[0092]

[0093] Constraints:

[0094]

[0095] δr c ≥δd ref

[0096] r safe ≤d ij ≤r ref

[0097] t s =t e

[0098] Φ u ≥K u

[0099] in, is the speed value of cluster individual i after being deceived, δv max is the maximum value of the speed deception offset, v max is the maximum speed that individual i in the UAV cluster can reach, r safe is the safe distance radius between cluster individuals, r ref is the effective communication distance radius between cluster individuals, Φ u is the overall coefficient of the cluster, K u is a constant coefficient, t e is the expected cheating time, t s is the actual cheating time, δr c Indicates the distance that the entire drone cluster is deceived, δd ref represents the expected deception distance, v irepresents the speed of individual i in the drone cluster, δv i (t) represents the deceptive offset of the speed due to navigation deception in the real satellite signal, and n represents the number of drones in the drone cluster.

[0100] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0101] In one embodiment, Figure 2 As shown, a satellite navigation deception device for swarming controlled drone clusters is provided, comprising: a deception offset derivation module 202, a satellite navigation deception model establishment module 204, a satellite navigation deception model solution module 206, and a drone cluster overall deception module 208, wherein:

[0102] The spoofing offset derivation module 202 is used to construct a dynamic model for swarming control of drone clusters; using the dynamic model, it derives the functional relationship between the satellite navigation spoofing offset and the position and velocity of the drone cluster, and calculates the distance that the entire drone cluster is deceived;

[0103] Establishing a satellite navigation deception model module 204 is configured to establish an objective function of the satellite navigation deception model based on a functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, set constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance, and the distance of the entire drone cluster that is deceiving; and establish the satellite navigation deception model based on the objective function and the constraints.

[0104] The satellite navigation deception model solving module 206 is configured to solve the satellite navigation deception model to obtain a deception offset; and to use the deception offset to generate a satellite navigation deception signal to deceive the swarming control drone cluster, successfully deceiving some of the drones in the cluster.

[0105] The drone cluster overall deception module 208 is used to deceive the speed of some drone cluster individuals under the action of the three principles of the swarming control algorithm to drive the change of the speed of the entire drone cluster. During the deception process, the direction of applying the deception offset is adjusted to avoid the speed and spacing of the individual drone clusters exceeding the extreme values, thereby achieving deception of the entire drone cluster.

[0106] Regarding the specific definition of a satellite navigation deception device for swarm-controlled drone clusters, please refer to the definition of a satellite navigation deception method for swarm-controlled drone clusters above, which will not be repeated here. The various modules in the above-mentioned satellite navigation deception device for swarm-controlled drone clusters can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0107] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A satellite navigation deception method for swarming controlled drone clusters, characterized in that: The method comprises: Construct a dynamic model for swarming drone control. Use the dynamic model to derive the functional relationship between the satellite navigation spoofing offset and the position and velocity of the drone cluster, and calculate the distance the entire drone cluster is deceived. Establishing an objective function of a satellite navigation deception model based on a functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster; setting constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance, and the distance at which the entire drone cluster is deceiving; and establishing a satellite navigation deception model based on the objective function and the constraints. Solving the satellite navigation spoofing model to obtain a spoofing offset; using the spoofing offset to generate a satellite navigation spoofing signal to spoof a swarming control drone cluster, successfully deceiving some of the drones in the cluster; Under the influence of the three principles of swarming control algorithm, the speed of some individual drones in the swarm is deceived to drive the change of the speed of the entire drone swarm. During the deception process, the direction of applying the deception offset is adjusted to prevent the speed and spacing of the individual drones in the swarm from exceeding the extreme values, thereby achieving deception of the entire drone swarm.

2. The method according to claim 1, characterized in that Construct a dynamic model for swarming control of drone clusters, including: The dynamic model of swarming control UAV cluster is constructed as Where α and β are constant coefficients, L is the Laplace matrix of the UAV cluster, I is the identity matrix, and: x=[P T V T a T ] T , a i are the acceleration of cluster individual i, p i ,v i ,u i are the position, velocity and control input of cluster individual i respectively.

3. The method according to claim 1, characterized in that The dynamic model is used to derive the functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, including: The dynamic model is used to derive the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster. The position of the cluster after the satellite signal is deceived is obtained as follows: Where, q=[q1 q1…q n ] T is the eigenvector corresponding to the zero eigenvalue of the Laplace matrix of the drone cluster, δp i ,δv i are the deceptive offsets of position and velocity caused by navigation deception in real satellite signals, dT is the interval between two adjacent moments, Δv i is the velocity change between two adjacent moments, t represents the moment, p i (t) represents the location information of the i-th UAV at time t, v i (t) represents the speed information of the i-th UAV at time t, and n represents the number of UAVs in the UAV cluster.

4. The method according to claim 3, characterized in that The method further comprises: The dynamic model is used to derive the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster. The speed of the cluster after the satellite signal is deceived is obtained as follows:

5. The method according to claim 1, wherein Calculate the distance that the entire drone cluster is deceived: Where, q=[q1 q1…q n ] T is the eigenvector corresponding to the zero eigenvalue of the Laplace matrix of the drone cluster, δp i ,δv i are the deceptive offsets of position and velocity caused by navigation deception in real satellite signals, dT is the interval between two adjacent moments, Δv i is the speed change between two adjacent moments, t represents the moment, and n represents the number of drones in the drone cluster.

6. The method according to claim 1, characterized in that A satellite navigation deception model is established according to the objective function and the constraints, including: According to the objective function and the constraints, a satellite navigation deception model is established: Constraints: δr c ≥δd ref r safe ≤d ij ≤r ref t s =t e F u ≥K u Among them, v max is the maximum speed that individual i in the drone cluster can reach, is the speed value of cluster individual i after being deceived, δv max is the maximum value of the speed deception offset, r safe is the safe distance radius between cluster individuals, r ref is the effective communication distance radius between cluster individuals, Φ u is the overall coefficient of the cluster, K u is a constant coefficient, t e is the expected cheating time, t s is the actual cheating time, δr c Indicates the distance that the entire drone cluster is deceived, δd ref represents the expected deception distance, v i represents the speed of individual i in the drone cluster, δv i (t) represents the deceptive offset of the speed due to navigation deception in the real satellite signal, and n represents the number of drones in the drone cluster.

7. A satellite navigation deception device for swarming drone clusters, characterized in that: The device comprises: The spoofing offset derivation module is used to build a dynamic model for swarming drone control. This model is used to derive the functional relationship between the satellite navigation spoofing offset and the position and velocity of the drone cluster, and to calculate the distance that the entire drone cluster has been deceived. Establishing a satellite navigation deception model module, which is used to establish an objective function of the satellite navigation deception model based on the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, and set constraints of the satellite navigation deception model based on the speed and safety distance of the drone cluster, the communication distance, and the distance of the drone cluster as a whole being deceived; and establishing the satellite navigation deception model based on the objective function and the constraints. a satellite navigation deception model solving module, configured to solve the satellite navigation deception model to obtain a deception offset; and generate a satellite navigation deception signal using the deception offset to deceive a swarming control drone cluster, successfully deceiving some of the drones in the cluster; The drone swarm overall deception module is used to deceive the speed of some individual drones in the swarm under the action of the three principles of the swarm control algorithm to drive the change of the speed of the entire drone swarm. During the deception process, the direction of applying the deception offset is adjusted to prevent the speed and spacing of individual drones in the swarm from exceeding the extreme values, thereby achieving deception of the entire drone swarm.

Images