Distributed UAV swarm countermeasure method and device based on GNSS spoofing

Abstract

The present application relates to a method and device for countering distributed drone clusters based on GNSS deception. The method includes: constructing a second-order dynamic model of a distributed drone cluster, using the second-order dynamic model to derive the coupling relationship between the GNSS deception amount and the position and speed of the drone cluster; using the slope of the ramp position deception signal to represent the size of the speed deception offset, and establishing a constraint condition for controlling the slope of the ramp position deception signal; using the constraint condition to optimize the slope of the ramp position deception signal, and adjusting the speed of the drone cluster by controlling the slope of the ramp position deception signal; using the consistency control principle of the cluster to successfully deceive the positioning information of some individuals in the drone cluster to drive the position offset of the entire drone cluster, thereby deceiving the entire target drone cluster. The use of this method can effectively solve the problem of cluster splitting and loss of control during the deception process.

Description

Technical Field

[0001] The present application relates to the field of GNSS spoofing technology, and in particular to a distributed drone cluster countermeasure method and device based on GNSS spoofing. Background Art

[0002] In recent years, drone swarms, thanks to their self-organization, adaptability, and robust self-healing capabilities, have been widely used in flight demonstrations, aerial mapping, search and rescue, environmental monitoring, and other fields, bringing significant economic and social benefits. However, with the widespread use of drone swarms, effective control and countermeasures have become a pressing issue in maintaining low-altitude airspace safety.

[0003] Soft-kill methods, such as GNSS spoofing, can seize control of drone swarms. While achieving regional no-fly zones, expelling and capturing drones, and forced landing and shooting down drones, they can effectively avoid collateral damage during drone swarm control and countermeasures, making them more applicable. Therefore, research on distributed drone swarm countermeasures based on GNSS spoofing is of great significance for controlling low-altitude drone swarms and maintaining low-altitude safety.

[0004] However, existing methods are primarily designed for individual drones and fail to consider the velocity disorder of a swarm during GNSS spoofing. Using existing methods to spoof GNSS against a swarm of drones can easily lead to swarm fragmentation and loss of control, resulting in severe collateral damage. Therefore, existing GNSS spoofing methods are unable to meet the needs of controlling and countering swarms of drones. Summary of the Invention

[0005] Based on this, it is necessary to address the above technical problems and provide a distributed drone cluster countermeasure method and device based on GNSS deception that can effectively solve the problem of cluster splitting and loss of control during the deception process and meet the needs of overall management and countermeasures of drone clusters.

[0006] A distributed drone swarm countermeasure method based on GNSS spoofing, the method comprising:

[0007] A second-order dynamic model of a distributed UAV swarm is constructed, and the coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV swarm is derived using the second-order dynamic model.

[0008] The slope of the ramped position deception signal is used to represent the magnitude of the speed deception offset, and a coupling relationship is used to establish a constraint condition for controlling the slope of the ramped position deception signal.

[0009] The slope of the ramp position deception signal is optimized using the constraint conditions, and the speed of the UAV cluster is adjusted by controlling the slope of the ramp position deception signal.

[0010] The cluster consistency control principle is used to successfully deceive the positioning information of some individuals in the drone cluster, causing the position of the entire drone cluster to shift, thereby achieving deception of the entire target drone cluster.

[0011] In one embodiment, building a second-order dynamic model of a distributed drone swarm includes:

[0012] The second-order dynamic model of the distributed UAV cluster is constructed as

[0013]

[0014] Where L is the Laplace matrix of the cluster, β is a constant coefficient, I is the identity matrix, and:

[0015] x=[P T V T ] T , p i ,v i are the position and velocity of cluster individual i, i = 1, 2,…, n.

[0016] In one embodiment, a second-order dynamic model is used to derive the coupling relationship between the GNSS spoofing amount and the position and velocity of the drone cluster, including:

[0017] The coupling relationship between the GNSS deception amount and the position of the UAV cluster is derived using the second-order dynamics model:

[0018]

[0019] Among them, q i is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, t is the time, t c The time it takes for the cluster to achieve consistency, 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.

[0020] In one embodiment, the coupling relationship between the GNSS spoofing amount and the UAV cluster speed is derived using a second-order dynamics model:

[0021]

[0022] Among them, q i is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, t is the time, t c The time it takes for the cluster to achieve consistency, 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.

[0023] In one embodiment, the slope of the ramped position spoofing signal is used to indicate the magnitude of the speed spoofing offset, including:

[0024] The slope of the ramp position deception signal is used to represent the magnitude of the speed deception offset.

[0025]

[0026] Among them, K d is the slope of the ramp position deception signal, t is time, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, respectively.

[0027] In one embodiment, optimizing the slope of the spoofing signal using constraints includes:

[0028] The process expression of optimizing the slope of the deception signal using the constraint conditions is:

[0029]

[0030] Constraints:

[0031]

[0032] δr c =δd ref

[0033] r safe ≤d ij ≤r ref

[0034] t s ≤t e

[0035] Φ u =1

[0036] Φ o ≥K o

[0037] in, is the speed value of cluster individual i after being deceived, v max is the maximum speed that an individual UAV cluster can reach; r safe is the safe distance radius between cluster individuals; r ref is the effective communication distance radius between individual drone clusters; Φ u is the integrity coefficient of the UAV cluster, Φ o K is the speed order of the drone cluster reaching the consistency state after being deceived, o is a constant coefficient, t e is the expected cheating time, t s is the actual cheating time, δd ref is the expected cluster deception distance, δr c is the distance that the entire drone cluster is actually deceived, K d is the slope of the ramp position deception signal, and n is the number of drones in the drone cluster.

[0038] A distributed drone swarm countermeasure device based on GNSS spoofing, the device comprising:

[0039] The coupling relationship derivation module is used to build a second-order dynamic model of the distributed UAV cluster and use the second-order dynamic model to derive the coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV cluster;

[0040] a constraint condition establishment module for using the slope of the ramped position deception signal to represent the magnitude of the speed deception offset and using a coupling relationship to establish a constraint condition for controlling the slope of the ramped position deception signal;

[0041] The deception signal optimization module is used to optimize the slope of the ramp position deception signal using constraints, and adjust the speed of the drone cluster by controlling the slope of the ramp position deception signal;

[0042] The drone cluster overall deception module is used to use the cluster consistency control principle to successfully deceive the positioning information of some individuals in the drone cluster, driving the position shift of the entire drone cluster, thereby achieving deception of the target drone cluster as a whole.

[0043] The above-mentioned distributed drone cluster countermeasure method and device based on GNSS deception, this application first constructs a second-order dynamic model of the distributed drone cluster, derives the coupling relationship between the GNSS deception amount and the drone cluster position and speed, then uses the slope of the ramped position deception signal to represent the size of the speed deception offset, and uses the coupling relationship to establish a constraint condition for controlling the slope of the ramped position deception signal, and then uses the constraint condition to optimize the slope of the deception signal, and uses the optimized deception signal slope to generate a ramped position deception signal to regulate the speed of the drone cluster. Finally, using the consistency control principle of the drone cluster, the position offset of the entire drone cluster is driven by the successful deception of the positioning information of some individuals in the drone cluster, thereby achieving deception of the target drone cluster as a whole. This application can drive the distributed drone cluster as a whole away from the target area or deceive it to a preset target position, while effectively avoiding collateral damage caused by the splitting and loss of control of the drone cluster during the deception process. It can be applied to scenarios of controlling and countering drone clusters in crowded places such as large-scale events and performances, as well as around important facilities such as oil depots. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 1 is a flow chart of a distributed drone swarm countermeasure method based on GNSS spoofing in one embodiment;

[0045] Figure 2 This is a structural block diagram of a distributed drone cluster countermeasure device based on GNSS spoofing in one embodiment. DETAILED DESCRIPTION

[0046] 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.

[0047] In one embodiment, Figure 1 As shown, a distributed drone swarm countermeasure method based on GNSS spoofing is provided, which includes the following steps:

[0048] Step 102: construct a second-order dynamic model of the distributed UAV cluster, and use the second-order dynamic model to derive the coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV cluster.

[0049] The second-order dynamic model of the distributed UAV cluster is constructed as

[0050]

[0051] Where L is the Laplace matrix of the cluster, β is a constant coefficient, I is the identity matrix, and: x=[P T V T ] T .p i ,v i are the position and velocity of cluster individual i, i = 1, 2,…, n.

[0052] The coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV cluster is derived using a second-order dynamic model, including:

[0053] The location of the drone cluster after the GNSS signal is spoofed is:

[0054]

[0055] Among them, q i is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, t is the time, t c The time it takes to achieve consistency for the cluster.

[0056] The speed of the drone cluster after the GNSS signal is spoofed is:

[0057]

[0058] Step 104 : Using the slope of the ramped position deception signal to represent the magnitude of the speed deception offset, and using the coupling relationship to establish a constraint condition for controlling the slope of the ramped position deception signal.

[0059] The speed deception offset represented by the slope of the ramp position deception signal is:

[0060]

[0061] Among them, K d is the slope of the position spoofing signal.

[0062] The constraint condition for controlling the slope of the ramp position deception signal is established as follows:

[0063]

[0064] Constraints:

[0065]

[0066] δr c =δd ref

[0067] r safe ≤d ij ≤rref

[0068] t s ≤t e

[0069] Φ u =1

[0070] Φ o ≥K o

[0071] in, is the speed value of cluster individual i after being deceived, v max is the maximum speed that the cluster individuals 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, Φ o K is the speed order of the cluster reaching the consistency state after the cluster is deceived, o is a constant coefficient, t e is the expected cheating time, t s is the actual cheating time, δd ref is the expected cluster deception distance, δr c is the actual deceived distance of the entire drone cluster.

[0072] And the actual deceived distance of the drone cluster as a whole is

[0073]

[0074] Step 106 , optimizing the slope of the ramped position deception signal using the constraint conditions, and adjusting the speed of the UAV cluster by controlling the slope of the ramped position deception signal.

[0075] The slope of the ramped position deception signal is optimized using constraints, and the above equation is solved under the constraints to obtain the optimal speed deception offset of the UAV cluster due to navigation deception. The speed of the UAV cluster can be adjusted by controlling the slope of the ramped position deception signal.

[0076] Step 108, using the cluster consistency control principle to successfully deceive the positioning information of some individuals in the drone cluster, drives the position shift of the entire drone cluster, and realizes deception of the entire target drone cluster.

[0077] The results of the optimization are used to generate deception signals to deceive the drone cluster. The successfully deceived drones will have their positions shifted due to changes in speed and positioning information. By utilizing the consistency control principle of the drone cluster, the position of the entire drone cluster can be shifted by successfully deceiving some individuals in the drone cluster, thereby achieving the purpose of driving the entire drone cluster away from the target area or deceiving it to the preset target position.

[0078] The above-mentioned distributed drone cluster countermeasure method based on GNSS deception, this application first constructs a second-order dynamic model of the distributed drone cluster, derives the coupling relationship between the GNSS deception amount and the drone cluster position and speed, then uses the slope of the ramped position deception signal to represent the size of the speed deception offset, and uses the coupling relationship to establish a constraint condition for controlling the slope of the ramped position deception signal, and then uses the constraint condition to optimize the slope of the deception signal, and uses the optimized deception signal slope to generate a ramped position deception signal to regulate the speed of the drone cluster. Finally, the consistency control principle of the drone cluster is used to successfully deceive the positioning information of some individuals in the drone cluster to drive the position offset of the entire drone cluster, thereby achieving deception of the target drone cluster as a whole. This application can drive the distributed drone cluster as a whole away from the target area or deceive it to a preset target position, while effectively avoiding collateral damage caused by the splitting and loss of control of the drone cluster during the deception process. It can be used in scenarios of controlling and countering drone clusters in crowded places such as large-scale events and performances, as well as around important facilities such as oil depots.

[0079] In one embodiment, building a second-order dynamic model of a distributed drone swarm includes:

[0080] The second-order dynamic model of the distributed UAV cluster is constructed as

[0081]

[0082] Where L is the Laplace matrix of the cluster, β is a constant coefficient, I is the identity matrix, and:

[0083] x=[P T V T ] T , p i ,v i are the position and velocity of cluster individual i, i = 1, 2,…, n.

[0084] In one embodiment, a second-order dynamic model is used to derive the coupling relationship between the GNSS spoofing amount and the position and velocity of the drone cluster, including:

[0085] The coupling relationship between the GNSS deception amount and the position of the UAV cluster is derived using the second-order dynamic model:

[0086]

[0087] Among them, q i is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, t is the time, t c The time it takes for the cluster to achieve consistency, 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.

[0088] In one embodiment, the coupling relationship between the GNSS spoofing amount and the speed of the UAV cluster is derived using the second-order dynamic model:

[0089]

[0090] Among them, q i is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, t is the time, t c The time it takes for the cluster to achieve consistency, 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.

[0091] In one embodiment, the slope of the ramped position spoofing signal is used to indicate the magnitude of the speed spoofing offset, including:

[0092] The slope of the ramp position deception signal is used to represent the magnitude of the speed deception offset.

[0093]

[0094] Among them, K d is the slope of the ramp position deception signal, t is time, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, respectively.

[0095] In one embodiment, optimizing the slope of the spoofing signal using constraints includes:

[0096] The process expression of optimizing the slope of the deception signal using the constraint conditions is:

[0097]

[0098] Constraints:

[0099]

[0100] δr c =δd ref

[0101] r safe ≤d ij ≤r ref

[0102] t s ≤t e

[0103] Φ u =1

[0104] Φ o ≥K o

[0105] in, is the speed value of cluster individual i after being deceived, v max is the maximum speed that an individual UAV cluster can reach; r safe is the safe distance radius between cluster individuals; r ref is the effective communication distance radius between individual drone clusters; Φ u is the integrity coefficient of the UAV cluster, Φ o K is the speed order of the drone cluster reaching the consistency state after being deceived, o is a constant coefficient, t e is the expected cheating time, t s is the actual cheating time, δd ref is the expected cluster deception distance, δr c is the distance that the entire drone cluster is actually deceived, K d is the slope of the ramp position deception signal, and n is the number of drones in the drone cluster.

[0106] 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 1At 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.

[0107] In one embodiment, Figure 2 As shown, a distributed UAV swarm countermeasure device based on GNSS spoofing is provided, comprising: a coupling relationship derivation module 202, a constraint condition establishment module 204, a spoofing signal optimization module 206 and a UAV swarm overall spoofing module 208, wherein:

[0108] A coupling relationship derivation module 202 is used to construct a second-order dynamic model of the distributed UAV cluster and use the second-order dynamic model to derive the coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV cluster;

[0109] A constraint condition establishing module 204 is configured to use the slope of the ramped position spoofing signal to represent the magnitude of the speed spoofing offset and to establish a constraint condition for controlling the slope of the ramped position spoofing signal using a coupling relationship;

[0110] The deception signal optimization module 206 is used to optimize the slope of the ramp position deception signal using the constraint conditions, and adjust the speed of the UAV cluster by controlling the slope of the ramp position deception signal;

[0111] The drone cluster overall deception module 208 is used to utilize the cluster consistency control principle to successfully deceive the positioning information of some individuals in the drone cluster, thereby driving the position shift of the entire drone cluster and achieving deception of the target drone cluster as a whole.

[0112] 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.

[0113] 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 distributed drone swarm countermeasure method based on GNSS spoofing, characterized by: The method comprises: A second-order dynamic model of a distributed UAV swarm is constructed, and the coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV swarm is derived using the second-order dynamic model. The slope of the ramped position deception signal is used to represent the magnitude of the speed deception offset, and the coupling relationship is used to establish a constraint condition for controlling the slope of the ramped position deception signal. The constraint condition is used to optimize the slope of the ramp position deception signal, and the speed of the UAV cluster is adjusted by controlling the slope of the ramp position deception signal; The cluster consistency control principle is used to successfully deceive the positioning information of some individuals in the drone cluster, causing the position of the entire drone cluster to shift, thereby achieving deception of the entire target drone cluster.

2. The method according to claim 1, characterized in that Construct a second-order dynamics model of a distributed UAV swarm, including: The second-order dynamic model of the distributed UAV cluster is constructed as Where L is the Laplace matrix of the cluster, β is a constant coefficient, I is the identity matrix, and: x=[P T V T ] T , p i ,v i are the position and velocity of cluster individual i, i = 1, 2,…, n.

3. The method according to claim 1, characterized in that The coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV cluster is derived using the second-order dynamic model, including: The coupling relationship between the GNSS deception amount and the position of the UAV cluster is derived using the second-order dynamic model: Among them, q i is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, t is the time, t c The time it takes for the cluster to achieve consistency, 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 coupling relationship between the GNSS deception amount and the UAV cluster speed is derived using the second-order dynamic model: Among them, q i is the eigenvector corresponding to the zero eigenvalue of L, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, t is the time, t c The time it takes for the cluster to achieve consistency, 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.

5. The method according to claim 1, wherein The slope of the ramp position deception signal is used to indicate the magnitude of the speed deception offset, including: The slope of the ramp position deception signal is used to represent the magnitude of the speed deception offset. Among them, K d is the slope of the ramp position deception signal, t is time, δp i ,δv i are the position and velocity spoofing offsets of GNSS signals, respectively.

6. The method according to claim 1, characterized in that Optimizing the slope of the deception signal using the constraint conditions includes: The process expression for optimizing the slope of the deception signal using the constraints is: Constraints: δr c =δd ref r safe ≤d ij ≤r ref t s ≤t e F u =1 F o ≥K o Among them, v i s (t s ) is the speed value of the cluster individual i after being deceived, vmax is the maximum speed value that the drone cluster individual can reach; r safe is the safe distance radius between cluster individuals; r ref is the effective communication distance radius between individual drone clusters; Φ u is the integrity coefficient of the UAV cluster, Φ o K is the speed order of the drone cluster reaching the consistency state after being deceived, o is a constant coefficient, t e is the expected cheating time, t s is the actual cheating time, δd ref is the expected cluster deception distance, δr c is the distance that the entire drone cluster is actually deceived, K d is the slope of the ramp position deception signal, and n is the number of drones in the drone cluster.

7. A distributed drone swarm countermeasure device based on GNSS spoofing, characterized by: The device comprises: A coupling relationship derivation module is used to construct a second-order dynamic model of a distributed UAV cluster and use the second-order dynamic model to derive the coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV cluster; a constraint condition establishment module, configured to use the slope of the ramped position deception signal to represent the magnitude of the speed deception offset, and to use the coupling relationship to establish a constraint condition for controlling the slope of the ramped position deception signal; A deception signal optimization module is used to optimize the slope of the ramp position deception signal using the constraint conditions, and adjust the speed of the drone cluster by controlling the slope of the ramp position deception signal; The drone cluster overall deception module is used to use the cluster consistency control principle to successfully deceive the positioning information of some individuals in the drone cluster, driving the position shift of the entire drone cluster, thereby achieving deception of the target drone cluster as a whole.

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