Distributed unmanned aerial vehicle cluster countering method and device based on GNSS deception

Abstract

The invention relates to a distributed unmanned aerial vehicle cluster countering method and device based on GNSS deception. The method comprises the following steps: constructing a second-order dynamic model of a distributed unmanned aerial vehicle cluster, and deducing a coupling relationship between GNSS deception amount and the position and speed of the unmanned aerial vehicle cluster by using the second-order dynamic model; the slope of the slope type position deception signal is used for representing the magnitude of the speed deception offset, and a constraint condition for controlling the slope of the slope type position deception signal is established; optimizing the slope of the slope type position deception signal by using a constraint condition, and controlling the slope of the slope type position deception signal to adjust the speed of the unmanned aerial vehicle cluster; the successful cheating of the positioning information of part of individuals in the unmanned aerial vehicle cluster by using the consistency control principle of the cluster drives the position offset of the whole unmanned aerial vehicle cluster, and the cheating of the whole target unmanned aerial vehicle cluster is realized. The method can effectively solve the problem of out-of-control cluster splitting in the deception process.

Description

Technical Field

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

[0002] In recent years, drone swarms have been widely used in flight performances, aerial mapping, search and rescue, environmental monitoring and other fields due to their advantages of self-organization, adaptability and robust self-healing, bringing huge economic and social benefits. With the popularization of drone swarm applications, how to effectively control and counter drone swarms has become an urgent problem to be solved in maintaining low-altitude safety.

[0003] Soft-kill methods represented by GNSS spoofing can seize control of drone clusters. While achieving regional no-fly zones, expelling capture, and forced landing and shooting down, they can effectively avoid collateral damage caused by drone cluster control and countermeasures, and have stronger applicability. Therefore, the study of distributed drone cluster countermeasures based on GNSS spoofing is of great significance for controlling low-altitude drone clusters and maintaining low-altitude safety.

[0004] However, the existing methods are mainly aimed at single drones, and have not yet considered the speed disorder problem of the cluster during the GNSS spoofing process. Using the existing methods to spoof GNSS on drone clusters is likely to cause the cluster to split and lose control, thereby causing serious collateral damage. Therefore, the existing GNSS spoofing methods are difficult to meet the control and countermeasure needs of drone clusters. Summary of the invention

[0005] Based on this, it is necessary to 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 for the drone cluster in response to the above technical problems.

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

[0007] A second-order dynamics model of a distributed UAV cluster is constructed, and the coupling relationship between the GNSS spoofing amount and the position and velocity of the UAV cluster is derived using the second-order dynamics 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 by using the constraint conditions, and the speed of the drone cluster is adjusted by controlling the slope of the ramp position deception signal.

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

[0011] In one embodiment, a second-order dynamic model of a distributed UAV cluster is constructed, including:

[0012] The second-order dynamics model of the distributed UAV swarm 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 a coupling relationship between the GNSS spoofing amount and the position and speed of the drone cluster, including:

[0017] The coupling relationship between the GNSS spoofing 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 to achieve consistency for the cluster, 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 speed of the drone cluster 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 constraints 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 drone 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 drone cluster, Φ o K is the speed order of the drone cluster reaching a consistent state after being deceived. o is a constant coefficient, t e is the expected cheating time, t s is the actual deception 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 represents the number of drones in the drone cluster.

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

[0039] The 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 speed of the UAV cluster.

[0040] A constraint condition establishment module, used 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;

[0041] The deception signal optimization module is used to optimize the slope of the ramp position deception signal by using the constraint conditions, 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 utilize the cluster consistency control principle to successfully deceive the positioning information of some individuals in the drone cluster, drive the position shift of the entire drone cluster, and achieve 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, and then uses the slope of the ramp position deception signal to represent the size of the speed deception offset, and uses the coupling relationship to establish constraints for controlling the slope of the ramp position deception signal, and then uses the constraints to optimize the slope of the deception signal, and uses the optimized deception signal slope to generate a ramp position deception signal to regulate the size of the drone cluster speed, and finally uses the consistency control principle of the drone cluster to drive the position offset of the entire drone cluster by successfully deceiving 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 away from the target area as a whole 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 large-scale events and performances and other crowded places as well as important facilities such as oil depots to control and counter drone clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a process of a distributed drone cluster countermeasure method based on GNSS spoofing in one embodiment;

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

[0046] 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.

[0047] In one embodiment, Figure 1 As shown, a distributed drone cluster countermeasure method based on GNSS spoofing is provided, including 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 speed of the UAV cluster.

[0049] The second-order dynamics model of the distributed UAV swarm 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 speed of the drone cluster is derived using the second-order dynamics 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 to achieve consistency for the cluster.

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

[0057]

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

[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 cluster integrity coefficient, Φ o is the speed order of the cluster reaching the consistent state after the cluster is deceived, K 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, using the constraint conditions 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.

[0075] The slope of the ramped position deception signal is optimized using constraints, and the above equations are 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 target drone cluster as a whole.

[0077] The results of optimization are used to generate deception signals to deceive the drone cluster. The successfully deceived drones will shift their positions 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, and then uses the slope of the ramp position deception signal to represent the size of the speed deception offset, and uses the coupling relationship to establish constraints for controlling the slope of the ramp position deception signal, and then uses the constraints to optimize the slope of the deception signal, and uses the optimized deception signal slope to generate a ramp position deception signal to regulate the size of the drone cluster speed, and finally uses the consistency control principle of the drone cluster to drive the position offset of the entire drone cluster by successfully deceiving 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 away from the target area as a whole 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 crowded places such as large-scale events and performances, as well as important facilities such as oil depots, to control and counter drone clusters.

[0079] In one embodiment, a second-order dynamic model of a distributed UAV cluster is constructed, including:

[0080] The second-order dynamics model of the distributed UAV swarm 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 a coupling relationship between the GNSS spoofing amount and the position and speed of the drone cluster, including:

[0085] The coupling relationship between the GNSS spoofing amount and the position of the UAV cluster is derived using the second-order dynamics 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 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 drone cluster is derived using the second-order dynamics 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 constraints 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 drone 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 drone cluster, Φ o K is the speed order of the drone cluster reaching a consistent 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 represents 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. Moreover, 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 cluster countermeasure device based on GNSS spoofing is provided, including: a coupling relationship derivation module 202, a constraint condition establishment module 204, a spoof signal optimization module 206 and a UAV cluster overall spoofing module 208, wherein:

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

[0109] A constraint condition establishment module 204, used to use the slope of the ramped position deception signal to represent the magnitude of the speed deception offset, and to establish a constraint condition for controlling the slope of the ramped position deception signal using a coupling relationship;

[0110] The deception signal optimization module 206 is used to optimize the slope of the ramp position deception signal by using the constraint conditions, and adjust the speed of the drone 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 to drive the position shift of the entire drone cluster, thereby achieving deception of the target drone cluster as a whole.

[0112] The technical features of the above embodiments may be arbitrarily combined. 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-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 distributed drone cluster countermeasure method based on GNSS spoofing, characterized in that: The method comprises: A second-order dynamic model of a distributed UAV cluster is constructed, and the coupling relationship between the GNSS spoofing amount and the position and speed of the UAV cluster 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 drone cluster is adjusted by controlling the slope of the ramp position deception signal; The consistency control principle of the cluster is used to successfully deceive the positioning information of some individuals in the drone cluster, leading to the position shift of the entire drone cluster, thereby achieving deception of the target drone cluster as a whole.

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 dynamics model of the distributed UAV swarm 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 speed of the drone cluster is derived using the second-order dynamic model, including: The coupling relationship between the GNSS spoofing amount and the position of the UAV cluster is derived using the second-order dynamics 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 to achieve consistency for the cluster, 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 spoofing amount and the UAV cluster speed is derived using the second-order dynamics 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, characterized in that The slope of the ramp position spoofing signal is used to indicate the magnitude of the speed spoofing 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 condition 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 drone cluster, Φ o K is the speed order of the drone cluster reaching a consistent state after being deceived. o is a constant coefficient, t e is the expected cheating time, t s is the actual deception 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 represents the number of drones in the drone cluster.

7. A distributed drone swarm countermeasure device based on GNSS spoofing, characterized in that: 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 speed of the UAV cluster; A constraint condition establishment module, used to use the slope of the ramped position deception signal to represent the magnitude of the speed deception offset, and 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 by 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 utilize the cluster consistency control principle to successfully deceive the positioning information of some individuals in the drone cluster, drive the position shift of the entire drone cluster, and achieve deception of the target drone cluster as a whole.

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