Satellite navigation deception method and device for swarm control unmanned aerial vehicle cluster

Abstract

The invention relates to a satellite navigation deception method and device for a swarm control unmanned aerial vehicle cluster. The method comprises the following steps: constructing a kinetic model of a swarm control unmanned aerial vehicle cluster; deriving a function relationship between the satellite navigation deception offset and the position and the speed of the unmanned aerial vehicle cluster by using a kinetic model, and calculating the deception distance of the whole unmanned aerial vehicle cluster; establishing an objective function of a satellite navigation deception model according to a function relationship between the satellite navigation deception offset and the position and the speed of the unmanned aerial vehicle cluster, and setting constraint conditions of the satellite navigation deception model according to the speed and the safety distance of the unmanned aerial vehicle cluster, the communication distance and the overall deception distance of the unmanned aerial vehicle cluster; solving the satellite navigation deception model to obtain a deception offset; and generating a satellite navigation deception signal by using the deception offset to carry out deception control on the swarm control unmanned aerial vehicle cluster so as to realize control. By adopting the method, the unmanned aerial vehicle cluster can be effectively managed, controlled and counteracted to swarming and control.

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] As a mainstream anti-UAV means, satellite navigation deception has unique technical advantages in the process of controlling and countering UAV swarms. However, with the continuous deepening of the study of biological swarm behavior in nature, researchers have extracted rules such as separation, aggregation and speed alignment from the intelligent swarm behavior of biological groups, developed the Flocking control algorithm and used it for the autonomous control of unmanned swarms, making the unmanned swarms gradually emerge with certain intelligent characteristics such as self-organization, adaptability, scalability and robust self-healing. When using existing deception methods to deceive the navigation of Flocking controlled UAV swarms, the deception will fail because the cluster individuals cannot achieve speed alignment and extreme value constraints at the same time, making it difficult to achieve the purpose of effectively controlling and countering Flocking controlled UAV 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 in response to the above-mentioned technical problems.

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

[0005] Construct a dynamic model of swarming control of drone clusters; use the dynamic model to derive the functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, and calculate the distance that the drone cluster is deceived as a whole;

[0006] The objective function of the satellite navigation deception model is established according to the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, and the constraints of the satellite navigation deception model are set according to the speed and safety distance of the drone cluster, the communication distance and the distance of the drone cluster as a whole being deceived; the satellite navigation deception model is established according to the objective function and the constraints;

[0007] The satellite navigation deception model is solved to obtain the deception offset; the deception offset is used to generate a satellite navigation deception signal to deceive the swarming control drone cluster, and some drones in the cluster are successfully deceived;

[0008] Under the influence of the three principles of swarming control algorithm, the speed of the entire drone cluster is changed by deceiving the speed of some individuals in the drone cluster. During the deception process, the direction of applying the deception offset is adjusted to avoid the individual speed and spacing of the drone cluster exceeding the extreme value, thereby achieving deception of the entire drone cluster.

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

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

[0011]

[0012] Among them, α, β are constant coefficients, L is the Laplace matrix of the drone 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 a position and a speed of a drone cluster, including:

[0014] 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. The position of the cluster after the satellite signal is spoofed 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 the real satellite signal, dT is the interval between two adjacent moments, Δv i is the speed 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 at which 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 the real satellite signal, 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 according to an objective function and constraint conditions, 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 drone 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 integrity 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 drone cluster is deceived as a whole, δd ref represents the expected deception distance, v i represents the speed of individual i in the drone cluster, δv i (t) represents the speed deception offset caused by 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 control of drone clusters, the device comprising:

[0033] The deception offset derivation module is used to build a dynamic model for swarming control of drone clusters. 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, and calculate the distance that the entire drone cluster is deceived.

[0034] A satellite navigation deception model module is established to establish an objective function of the satellite navigation deception model according to the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, and to set constraints of the satellite navigation deception model according to the speed and safety distance of the drone cluster, the communication distance and the distance of the drone cluster as a whole being deceived; a satellite navigation deception model is established according to 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, and some drones in the cluster are successfully deceived;

[0036] The overall deception module of the drone cluster is used to deceive the speed of some individuals in the drone cluster 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 prevent the individual speed and spacing of the drone cluster from exceeding the extreme value, thereby achieving deception of the entire drone cluster.

[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 drone cluster as a whole being deceived, establishes the objective function of the satellite navigation deception model according to 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 according to the speed and safety distance of the drone cluster, the communication distance and the distance of the drone cluster as a whole being deceived; establishes the satellite navigation deception model according to 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, and 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 applying the deception offset is adjusted to avoid the speed and spacing of the cluster individuals exceeding the extreme value, thereby achieving deception of the cluster as a whole. The present application can deceive the swarm-controlled drone cluster to a pre-set target position, improve the deception effect on the overall drone cluster, and can be applied to various scenarios for controlling and countering illegal drone clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a flow chart of a satellite navigation spoofing method for swarming controlled drone clusters in one embodiment;

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

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present 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 swarming control of drone clusters; use the dynamic model to derive the functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, and calculate the distance that the entire drone cluster is deceived.

[0043] The dynamic model of flocking control of drone clusters is constructed as follows:

[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 cluster individual i under the influence of 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 the real satellite signal, Δv i is the velocity change between two adjacent moments.

[0064] Step 104, establish the objective function of the satellite navigation deception model according to 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 according to 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; establish the satellite navigation deception model according to 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 individual i in the cluster 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 integrity 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, solving the satellite navigation deception model to obtain a deception offset; using 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.

[0075] Step 108, under the influence of the three principles of the swarming control algorithm, the speed of the entire drone cluster is changed by deceiving the speed of some individuals in the drone cluster. During the deception process, the direction of applying the deception offset is adjusted to prevent the individual speed and spacing of the drone cluster from exceeding the extreme value, 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 drone cluster as a whole being deceived, establishes the objective function of the satellite navigation deception model according to 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 according to the speed and safety distance of the drone cluster, the communication distance and the distance of the drone cluster as a whole being deceived; establishes the satellite navigation deception model according to the objective function and the constraints, solves the satellite navigation deception model to obtain a method capable of maintaining the drone cluster The deception offset of group 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, and 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 applying the deception offset is adjusted to avoid the speed and spacing of the cluster individuals exceeding the extreme value, thereby achieving deception of the cluster as a whole. The present application can deceive the swarm-controlled drone cluster to a pre-set target position, improve the deception effect on the overall drone cluster, and can be applied to various scenarios for controlling and countering illegal drone clusters.

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

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

[0079]

[0080] Among them, α, β are constant coefficients, L is the Laplace matrix of the drone 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 a position and a speed of a drone cluster, including:

[0082] 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. The position of the cluster after the satellite signal is spoofed 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 the real satellite signal, dT is the interval between two adjacent moments, Δv i is the speed 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 at which the entire drone cluster is deceived is calculated as

[0088]

[0089] Where, q=[q1 q1…qn ] 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 the real satellite signal, 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 according to an objective function and constraint conditions, 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 drone 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 integrity 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 drone cluster is deceived as a whole, δd ref represents the expected deception distance, v i represents the speed of individual i in the drone cluster, δv i(t) represents the speed deception offset caused by 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. Moreover, 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 control of drone clusters is provided, including: 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 deception offset derivation module 202 is used to construct a dynamic model of swarming control of drone clusters; 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, and calculate the distance of the entire drone cluster being deceived;

[0103] Establishing a satellite navigation deception model module 204, used to establish an objective function of the satellite navigation deception model according to the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, set the constraint conditions of the satellite navigation deception model according to 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 according to the objective function and the constraint conditions;

[0104] The satellite navigation deception model solving module 206 is used to 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;

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

[0106] For 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. Each module in the above-mentioned satellite navigation deception device for swarm-controlled drone clusters can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0107] The technical features of the above embodiments may 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-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A satellite navigation deception method for swarming control of drone clusters, characterized in that: The method comprises: Construct a dynamic model of swarming control of drone clusters; use the dynamic model to derive the functional relationship between the satellite navigation spoofing offset and the position and speed of the drone cluster, and calculate the distance that the drone cluster is deceived as a whole; Establishing an objective function of a satellite navigation deception model according to a functional relationship between a satellite navigation deception offset and the position and speed of a drone cluster, setting constraints of the satellite navigation deception model according to 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; establishing a satellite navigation deception model according to the objective function and the constraints; The satellite navigation deception model is solved to obtain a deception offset; the deception offset is used to generate a satellite navigation deception signal to deceive the swarming control drone cluster, and some drones in the cluster are successfully deceived; Under the influence of the three principles of swarming control algorithm, the speed of the entire drone cluster is changed by deceiving the speed of some individuals in the drone cluster. During the deception process, the direction of applying the deception offset is adjusted to avoid the individual speed and spacing of the drone cluster exceeding the extreme value, thereby achieving deception of the entire drone cluster.

2. The method according to claim 1, characterized in that Construct a dynamic model of swarming control of drone clusters, including: The dynamic model of swarming control of UAV clusters is constructed as Among them, α, β are constant coefficients, L is the Laplace matrix of the drone 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 spoofing offset and the position and speed of the drone cluster. The position of the cluster after the satellite signal is spoofed 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 the real satellite signal, dT is the interval between two adjacent moments, Δv i is the speed 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 spoofing offset and the position and speed of the drone cluster. The speed of the cluster after the satellite signal is spoofed is obtained as follows:

5. The method according to claim 1, characterized in that Calculate the distance that the drone cluster is deceived as a whole: 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 the real satellite signal, 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 integrity 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 drone cluster is deceived as a whole, δd ref represents the expected deception distance, v i represents the speed of individual i in the drone cluster, δv i (t) represents the speed deception offset caused by 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 control of drone clusters, characterized in that: The device comprises: The deception offset derivation module is used to build a dynamic model for swarming control of drone clusters. 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, and calculate the distance that the entire drone cluster is deceived. Establishing a satellite navigation deception model module, which is used to establish an objective function of the satellite navigation deception model according to the functional relationship between the satellite navigation deception offset and the position and speed of the drone cluster, and set the constraint conditions of the satellite navigation deception model according to the speed and safety distance of the drone cluster, the communication distance and the distance of the drone cluster as a whole being deceived; establishing the satellite navigation deception model according to the objective function and the constraint conditions; A satellite navigation deception model solving module is used to 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; The overall deception module of the drone cluster is used to deceive the speed of some individuals in the drone cluster 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 prevent the individual speed and spacing of the drone cluster from exceeding the extreme value, thereby achieving deception of the entire drone cluster.

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