A countermeasure method and system for unmanned aerial vehicle swarm attack

Abstract

The application discloses a kind of anti-measures method and system for unmanned aerial vehicle cluster attack, belong to unmanned aerial vehicle anti-measures technical field.The method of the present application is by the target information detected to radar detection, radio detection, optical detection etc. equipment detection is preprocessed unified data structure, and multi-source data is fused using fusion algorithm, and the integrated information of unmanned aerial vehicle cluster target is generated;Combined with the integrated information of unmanned aerial vehicle cluster target, by analyzing the factors influencing the degree of unmanned aerial vehicle threat, such as unmanned aerial vehicle type, relative speed of unmanned aerial vehicle, flight height of unmanned aerial vehicle, distance between unmanned aerial vehicle and control area, unmanned aerial vehicle cluster threat evaluation index system is constructed, and the TOPSIS sorting method based on entropy weight is used to realize unmanned aerial vehicle cluster threat sorting;Finally, according to the unmanned aerial vehicle cluster threat sorting result, a kind of unmanned aerial vehicle group anti-measures decision method based on anti-measures equipment performance is proposed, to realize accurate unmanned aerial vehicle cluster anti-measures decision.

Description

Technical Field

[0001] This invention belongs to the field of drone countermeasures technology, and more specifically, relates to a countermeasure method and system for drone swarm attacks. Background Technology

[0002] With the booming development of drones in agriculture, military, and commerce, their use has exploded, leading to a rise in incidents of drones illegally intruding into important areas for covert filming and attacks. Currently, drone attack methods are becoming increasingly sophisticated, employing various techniques such as drone swarm attacks, swarm attacks carrying explosives, and lone drones conducting covert reconnaissance and filming. These attacks target key objectives, areas, and individuals under protection, with government buildings, ports, water conservancy projects, and military strongholds being particularly vulnerable.

[0003] Traditional drone countermeasures systems typically employ radar detection combined with hard-kill attacks or radio detection combined with radio jamming to target individual illegal drones. However, due to the characteristics of swarm drones—such as large numbers, small size, low flight altitude, relatively slow speed compared to other aircraft, and strong coordination capabilities—traditional drone countermeasures suffer from blind spots in detection, difficulty in rationally allocating countermeasure targets, and excessively long countermeasure times. This makes it extremely easy for some drones in the swarm to penetrate defenses and then conduct reconnaissance or attacks on personnel or facilities in key protected areas, posing a significant threat to public safety. Summary of the Invention

[0004] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a countermeasure method and system for drone swarm attacks, which aims to effectively improve the success rate and efficiency of intercepting and dealing with drone swarms.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a countermeasure method against drone swarm attacks, the method comprising:

[0006] The system collects detection data from multiple sources of sensors. After preprocessing the detection data, it performs spatiotemporal alignment, data association, trajectory tracking filtering, and target fusion to obtain target state data. Then, it uses a standard database and a target recognition feature library to perform target recognition. Combining the target state data, the system forms the final target state information. The multiple sources of sensors include radar, radio detection equipment, and optical detection equipment.

[0007] Based on the drone type, relative speed, flight altitude, distance between the drone and the control area, and the criticality of the drone communication network in the target status information, a drone swarm threat evaluation index system is constructed, and the TOPSIS ranking method based on entropy weight is used to rank drone threats.

[0008] Countermeasure decisions are made by combining the drone threat ranking results with the performance parameters of the countermeasure equipment.

[0009] Preferably, the acquisition of multi-source sensor detection data specifically includes:

[0010] When the radar does not detect the target, but the radio detection equipment does, it collects the radio detection track.

[0011] When radar detects a target but radio detection equipment does not, radar track is collected;

[0012] When radar and radio detection equipment detect a target simultaneously, radar tracks are collected first, while the UAV model and frequency identified by the radio detection equipment are collected at the same time.

[0013] When radar and optical detection equipment detect a target simultaneously, the detection data from both radar and optical detection equipment are collected at the same time.

[0014] Preferably, the step of obtaining target state data by performing spatiotemporal alignment, data association, trajectory tracking filtering, and target fusion after preprocessing the detection data specifically includes the following steps:

[0015] The probe data is converted into a unified data format according to the protocol and format.

[0016] Identify and remove outliers from the detection data;

[0017] Compress the detection data;

[0018] Perform time and space calibration on the probe data;

[0019] For the multi-target detection data collected by each sensor, the data is correlated based on the trajectory association algorithm according to the sensor accuracy and data dimension to determine which target the collected detection data belongs to;

[0020] An unscented Kalman filter algorithm is used to perform track tracking filtering on the target trajectory.

[0021] Multi-sensor trajectory information of the same target is fused and processed to achieve target tracking.

[0022] Preferably, the drone swarm threat assessment index system specifically includes:

[0023] Drone type threat: The size of the drone is obtained based on the target status information, and the threat level is classified according to the size of the drone. The smaller the drone, the higher the threat level.

[0024] The threat of relative speed of drones is determined by obtaining the speed v of the drone relative to the control area based on target state information.′ :

[0025]

[0026] Where v is the speed of the UAV; β is the angle of the UAV relative to the center point of the control area; α is the flight heading of the UAV; and the speed v ′ The higher the number, the higher the threat level;

[0027] Drone flight altitude threat: The drone's flight altitude is obtained based on target status information. The lower the flight altitude, the higher the threat level.

[0028] The threat level is determined by the distance between the drone and the control area, based on the target status information, by obtaining the distance between the drone and the center point of the control area. The smaller the distance, the higher the threat level.

[0029] The critical threat to drones in swarm communication networks; the criticality of drone i in swarm communication networks based on target state information. i :

[0030]

[0031] in, U represents the number of shortest paths between drone node pairs a and b that pass through drone i. ab This represents the total number of shortest paths between drone node pairs a and b;

[0032] Key C i The higher the number, the higher the threat level.

[0033] Preferably, the TOPSIS ranking method based on entropy weight specifically includes the following steps:

[0034] The five evaluation indicators in the drone swarm threat evaluation index system were normalized.

[0035]

[0036] Among them, Index ij I represents the j-th evaluation metric for the i-th UAV in the cluster after normalization. ij This represents the j-th evaluation metric for the i-th drone in the cluster;

[0037] Calculate the entropy value H of the j-th evaluation index based on the normalization result. j The entropy weight w of the j-th evaluation index j ;

[0038]

[0039] k = lnm

[0040]

[0041] Construct the normalized matrix A;

[0042]

[0043] Determine the ideal point A + and negative ideal point A - ;

[0044]

[0045]

[0046] Among them, positive indicators are those whose larger values ​​indicate a higher threat; negative indicators are those whose larger values ​​indicate a lower threat. 1≤i≤n Index ij and min 1≤i≤n Index ij These represent the maximum and minimum values ​​of the j-th evaluation indicator within the evaluation area, respectively.

[0047] Calculate A ij Distance to each ideal point A ij Distance to each negative ideal point

[0048]

[0049]

[0050] Calculate the threat level σ of drone i i ;

[0051]

[0052] Preferably, the countermeasure decision is made by combining the drone threat ranking results and the performance parameters of the countermeasure equipment, specifically as follows:

[0053] Based on the drone threat ranking, the following actions will be taken for each drone in descending order of threat level:

[0054] (1) Let the distance between the UAV and the control center point be d. UVA Obtain the minimum effective range of each soft-kill countermeasure device. and maximum effective range If a countermeasure device i exists, it satisfies If the condition is met, proceed to step (2); otherwise, proceed to step (6).

[0055] (2) Combining the distance d between the drone and the control center point UVA and the relative flight speed v of the drone ′ Calculate the time t for the drone to fly to the control area. uva :

[0056] t uva = UVA / ′

[0057] (3) Obtain the maximum response time {t1, ..., t2} of each device. n If a countermeasure device i exists that satisfies If the condition is met, proceed to step (4); otherwise, proceed to step (6).

[0058] (4) Obtain the usage status of countermeasure device i. If the device is unused, and If the condition is met, proceed to step (5); otherwise, proceed to step (6).

[0059] (5) For countermeasure device i that meets the distance, time and usage status constraints, make countermeasure decisions in the order of acoustic interference, electromagnetic interference, navigation interference and navigation deception. If the UAV is not successfully countered in the above order, proceed to step (6).

[0060] (6) If the soft kill countermeasure fails, the target UAV is destroyed by hard kill countermeasure. The hard kill countermeasure includes lasers, missiles and loitering munitions.

[0061] Secondly, the present invention provides a countermeasure system against drone swarm attacks, the system comprising:

[0062] The data acquisition and processing module is used to acquire detection data from multiple sources of sensors. After preprocessing the detection data, it performs spatiotemporal alignment, data association, trajectory tracking filtering, and target fusion to obtain target state data. Then, it uses a standard database and a target recognition feature library to perform target recognition. Combining the target state data, it forms the final target state information. The multiple sources of sensors include radar, radio detection equipment, and optical detection equipment.

[0063] The threat ranking module is used to construct a drone swarm threat evaluation index system based on the drone type, drone relative speed, drone flight altitude, distance between the drone and the control area, and drone criticality of the swarm drone communication network contained in the target status information, and to achieve drone threat ranking using the TOPSIS ranking method based on entropy weight.

[0064] The countermeasure decision module is used to make countermeasure decisions by combining the drone threat ranking results and the performance parameters of the countermeasure equipment.

[0065] Preferably, the threat ranking module constructs a drone swarm threat evaluation index system through the following units:

[0066] The drone type threat unit is used to obtain the size of the drone based on the target status information and classify the threat level according to the size of the drone. The smaller the drone, the higher the threat level.

[0067] The drone relative speed threat unit is used to obtain the drone's speed v relative to the control area based on target state information. ′ :

[0068]

[0069] Where v is the speed of the UAV; β is the angle of the UAV relative to the center point of the control area; α is the flight heading of the UAV; and the speed v ′ The higher the number, the higher the threat level;

[0070] The drone flight altitude threat unit is used to obtain the drone's flight altitude based on target status information. The lower the flight altitude, the higher the threat level.

[0071] The distance threat unit between the drone and the control area is used to obtain the distance between the drone and the center point of the control area based on the target status information. The smaller the distance, the higher the threat level.

[0072] The swarm drone communication network's key threat unit is used to determine the criticality (C) of drone i within the swarm communication network based on target status information. i :

[0073]

[0074] in, U represents the number of shortest paths between drone node pairs a and b that pass through drone i. ab This represents the total number of shortest paths between drone node pairs a and b;

[0075] Key C i The higher the number, the higher the threat level.

[0076] Preferably, the threat ranking module performs drone threat ranking by executing the following units in sequence:

[0077] The normalization unit is used to normalize the five evaluation indicators in the drone swarm threat evaluation indicator system.

[0078]

[0079] Among them, Index ijI represents the j-th evaluation metric for the i-th UAV in the cluster after normalization. ij This represents the j-th evaluation metric for the i-th drone in the cluster;

[0080] The entropy weight calculation unit is used to calculate the entropy value H of the j-th evaluation index based on the normalization result. j The entropy weight w of the j-th evaluation index j ;

[0081]

[0082]

[0083] Matrix computation unit, used to construct normalized matrix A;

[0084]

[0085] Ideal point calculation unit, used to determine ideal point A + and negative ideal point A - ;

[0086]

[0087]

[0088] Among them, positive indicators are those whose larger values ​​indicate a higher threat; negative indicators are those whose larger values ​​indicate a lower threat. 1≤i≤n Index ij and min 1≤i≤n Index ij These represent the maximum and minimum values ​​of the j-th evaluation indicator within the evaluation area, respectively.

[0089] Distance calculation unit, used to calculate A ij Distance to each ideal point A ij Distance to each negative ideal point

[0090]

[0091]

[0092] Threat calculation unit, used to calculate the threat level σ of drone i. i ;

[0093]

[0094] Preferably, the countermeasure decision module specifically includes the following units executed sequentially:

[0095] Based on the drone threat ranking, perform the following unit operations on each drone in descending order of threat level:

[0096] The distance determination unit is used to set the distance between the UAV and the control center point as d. UVA Obtain the minimum effective range of each soft-kill countermeasure device. and maximum effective range If a countermeasure device i exists, it satisfies Then proceed to the speed determination unit; otherwise proceed to the hard kill countermeasure unit.

[0097] The speed determination unit is used to determine the distance d between the drone and the control center point. UVA and the relative flight speed v of the drone ′ Calculate the time t for the drone to fly to the control area. uva :

[0098] t uva = UVA / ′

[0099] The time determination module is used to obtain the maximum response time {t1, ...,t} of each device. n If a countermeasure device i exists that satisfies If the status is determined, proceed to the status judgment unit; otherwise, proceed to the hard kill countermeasure unit.

[0100] The status determination unit is used to obtain the usage status of the countermeasure device i. If the device is not in use, and If the attack fails, the system will switch to the soft-kill countermeasure unit; otherwise, it will switch to the hard-kill countermeasure unit.

[0101] The soft-kill countermeasure unit is used to make countermeasure decisions against countermeasure devices i that meet the constraints of distance, time and usage status, in the order of acoustic interference, electromagnetic interference, navigation interference and navigation deception. If the UAV is not successfully countered in the above order, the countermeasure is transferred to the hard-kill countermeasure unit.

[0102] A hard-kill countermeasure unit is used to destroy the target UAV when soft-kill countermeasures fail. The hard-kill countermeasures include lasers, missiles, and loitering munitions.

[0103] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0104] (1) This invention proposes a multi-source fusion-based UAV swarm reconnaissance and detection method. By preprocessing the target information detected by radar detection, radio detection, optical detection and other equipment into a unified data structure, and using a fusion algorithm to fuse the multi-source data, comprehensive information of swarm UAV targets is generated. This method adopts a complementary fusion of multiple detection methods to overcome the problems of large detection blind spots, easy interference, insufficient accuracy and high error rate of existing UAV swarm detection methods, and improves the accuracy of UAV swarm identification.

[0105] (2) This invention proposes a multi-factor identification-based method for ranking drone swarm threats. It comprehensively considers the flight characteristics of drone swarms, obtains drone identification information from various detection devices to understand the platform capabilities of the drones, and combines factors affecting the threat level of drones, such as drone type, relative speed, flight altitude, distance between the drone and the control area, and the criticality of the drone's communication network. A drone swarm threat evaluation index system is constructed, and the TOPSIS ranking method based on entropy weight is used to achieve drone swarm threat ranking. This method comprehensively considers the key factors affecting the threat level of drones and can effectively provide reasonable support for the countermeasure decision-making order of drones in a drone swarm.

[0106] (3) Combining the threat ranking results of UAVs with the performance parameters of countermeasure equipment, a UAV swarm countermeasure decision-making method based on the performance of countermeasure equipment is proposed. This method effectively shortens the countermeasure decision-making time and improves the countermeasure efficiency and success rate. Attached Figure Description

[0107] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0108] Figure 2 This is a schematic diagram of a UAV swarm reconnaissance and detection method based on multi-source fusion in an embodiment of the present invention;

[0109] Figure 3 This is a schematic diagram of the relative speed of the UAV in an embodiment of the present invention;

[0110] Figure 4 This is a diagram of the drone threat indicator system in this embodiment of the invention. Detailed Implementation

[0111] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0112] like Figure 1 As shown, the embodiments of the present invention include the following steps:

[0113] Step 1: A multi-source fusion-based UAV swarm reconnaissance and detection method:

[0114] The first step in countering drone swarms in key protected areas is detecting drone targets within the protected area's coverage. To better achieve reconnaissance and detection of drone swarms near the protected area, this invention proposes a multi-source fusion-based drone swarm reconnaissance and detection method. This method integrates radar, radio detection, and photoelectric infrared sensors for collaborative target detection. Radio detection equipment can quickly acquire target positions, and multiple stations can achieve cross-location. It can identify drone target frequencies and models, but its disadvantages include lower positioning accuracy and a higher false alarm rate in complex electromagnetic environments. Radar equipment can achieve all-weather, all-time active detection with high positioning accuracy, but its disadvantages include the inability to determine target type and sensitivity to target obstruction. Photoelectric tracking equipment can identify target images, but its disadvantage is that it requires radar guidance for searching. In summary, each sensor has its own advantages and disadvantages. To better achieve continuous tracking and accurate identification of drone targets, multiple sensors need to work collaboratively, complementing each other's strengths. The multi-source fusion-based drone swarm reconnaissance and detection method proposed in this invention, such as... Figure 2 As shown, the specific steps are as follows:

[0115] 1. Information Collection: Collect data reported by radar detection equipment, radio detection equipment, and optical detection equipment, including target tracks, point tracks, spectrum, etc.

[0116] 1) When the radar does not detect the target but the radio detects the target, the radio track is used for detection;

[0117] 2) When the radar detects the target but the radio detection equipment does not, the radar track is used;

[0118] 3) When radar and radio detection detect a target simultaneously, radar tracks should be used first, while radio detection should be used to identify the UAV's model and frequency.

[0119] 4) When radar and electro-optical infrared are tracking simultaneously, the radar distance is correlated and fused with the azimuth and elevation of the electro-optical infrared. See step 2 for details.

[0120] 2. Multi-source data fusion: Classify and preprocess the received target information of various types, and correlate and synthesize the preprocessed target feature vectors, image intelligence and other information.

[0121] 1) Preprocessing:

[0122] A. Convert the collected target data according to the protocol to form a unified data format;

[0123] B. Identify and remove outlier data;

[0124] C. Compress the data to reduce the amount of data transmitted.

[0125] 2) Real-time data fusion

[0126] A. Spacetime Alignment

[0127] Because radar, radio detection, photoelectric infrared, and other sensors have different functions, performance, and detection principles, their data is heterogeneous. Therefore, in the fusion positioning process, the targets of each information source must first be time-calibrated using a time synchronization system. Then, using a unified coordinate system as the reference coordinate system, the detection data from each information officer are spatially calibrated. After the spatiotemporal calibration is completed, the collected multi-source data undergoes protocol and format transformation to convert it into a unified data format.

[0128] B. Data Association

[0129] For the multi-target data information collected by each sensor, the trajectory association algorithm should perform data association based on the detector accuracy and data dimension to determine which target the collected positioning data belongs to.

[0130] C. Tracking Filtering

[0131] An unscented Kalman filter algorithm is used for trajectory tracking filtering.

[0132] D. Goal Fusion

[0133] After performing trajectory association and multi-target state estimation on the data from each sensor, the multi-sensor trajectory information of the same target is fused to achieve target tracking.

[0134] 3. Comprehensive target identification: The standard database and target identification feature library are used to identify targets at the feature level, and decision rules are used to identify targets comprehensively. The final target status information is formed by combining the fused target status data.

[0135] Step 2: Threat ranking method for drone swarms based on multi-factor identification:

[0136] Combining the information on drone morphology, location, and flight altitude obtained in step one, and addressing the issue that traditional drone threat ranking methods often consider individual drone type, speed, altitude, and angle performance indicators but fail to extract effective indicators for the self-organizing network characteristics of drone swarms, this invention proposes a drone swarm threat ranking method based on multi-factor identification. This method comprehensively considers the flight characteristics of drone swarms, uses drone identification information obtained from various detection devices to determine the platform capabilities of the drones, and combines factors influencing the threat level of drones, such as drone type, relative speed, flight altitude, distance between the drone and the control area, and the criticality of the drone in the swarm's communication network, to construct a drone swarm threat evaluation index system. The TOPSIS ranking method based on entropy weight is then used to achieve drone swarm threat ranking.

[0137] Drones differ from typical low-altitude attack targets in that they are characterized by their suddenness, difficulty in detection, and simple attack patterns. Therefore, when assessing their threat level, it is necessary to comprehensively consider various factors that affect the degree of threat posed by drones.

[0138] 1. Construction of a drone threat indicator system based on multi-factor identification:

[0139] 1) Types of drones

[0140] In drone swarm attacks, multiple types of drones are typically used in swarm operations. Therefore, this invention considers drone type as an important indicator of the threat level posed by drones. Since radar, radio detection, and photoelectric detection equipment can only acquire limited information about drones, this invention classifies drones according to their size: micro drones, light drones, small drones, and large drones, corresponding to levels 1 to 4. Based on the principles of drone reconnaissance and detection, smaller drones are more difficult to detect; therefore, smaller drones pose a higher threat to the controlled area.

[0141] 2) Relative speed of the drone

[0142] The relative speed of a drone refers to its speed relative to the controlled area. This speed directly affects the difficulty and success rate of drone countermeasures. Generally, the value is positive in the direction of approach and negative in the direction of departure. The higher the relative speed, the greater the threat posed by the drone to the controlled area. Relative speed is an important indicator for comprehensively evaluating the threat posed by drone speed and direction of movement.

[0143] from Figure 3 As can be seen from this, the drone's operating speed v, distance D from the center point of the control area, flight heading α, and angle β relative to the center point of the control area can be obtained through various detection devices. Therefore, the drone's relative speed v... ′ It can be calculated using the following formula:

[0144]

[0145] 3) Drone flight altitude

[0146] The flight altitude of drones can be obtained using radar and radio detection equipment. Because drones fly at low altitudes, the lower their altitude, the lower the probability of detection and the higher the threat they pose to the defense area. Therefore, the flight altitude of drones is an important factor in evaluating threat defense areas.

[0147] 4) Distance between the drone and the control area

[0148] The distance between a drone and the control area refers to the distance between the drone and the center point of the control area. During the drone's flight, the smaller the distance between the drone and the control area, the higher the threat to the control area.

[0149] 5) Swarm UAV communication network: the key role of UAVs

[0150] When drone swarms perform attack and reconnaissance missions, self-organizing networks are often used to ensure smooth communication between swarms. A drone swarm self-organizing network refers to a complex network where each drone acts as an independent communication node, relying on its own communication payload and dynamically adjusting its communication links according to the actual internal and external environmental requirements. In a drone swarm communication network, the criticality of each drone within the network is assessed by intercepting communication signals, thus determining the drone's importance within the network. The stronger a drone's criticality, the higher its importance within the network, the more drones rely on it for communication, and the greater the threat posed to the controlled area.

[0151] This invention employs a betweenness centrality index method to evaluate the criticality of drones in a swarm drone communication network. Specifically, the higher the frequency of drone i on the shortest path among all pairs of drone nodes in the swarm communication network, the greater the betweenness centrality of drone i, and the higher its criticality. The calculation formula is as follows:

[0152]

[0153] In the above formula, C i For the communication network of swarm drones, drone i is of critical importance. U represents the number of shortest paths between drone node pairs a and b that pass through drone i. ab This represents the total number of shortest paths between drone node pairs a and b.

[0154] Based on the above factors, a drone threat indicator system is constructed as follows: Figure 4 .

[0155] 2. Entropy weight-based TOPSIS drone swarm threat ranking method:

[0156] Based on the drone threat index system constructed in step 1, the TOPSIS method based on entropy weights is used to rank the threats of m drones in the drone swarm. Specifically, first, the entropy weight method is used to objectively determine the weights of the benchmark indicators, and then the TOPSIS ranking method, which approximates the ideal solution, is used to rank and evaluate the novel drone threats in the swarm. The specific steps are as follows:

[0157] 1) Determining the weights of indicators using the entropy weight method:

[0158] A. Normalize the five indicators in the identification of drone swarm threat factors:

[0159]

[0160] In the formula, Index ij I represents the normalized evaluation metric for the i-th UAV in the cluster. ij Let represent the j-th evaluation metric of the i-th drone in the cluster, and m represent the number of drones in the drone cluster.

[0161] B. Calculate the entropy value H of the j-th index based on the normalization result. j The entropy weight w of the j-th index j :

[0162]

[0163]

[0164] 2) TOPSIS sorting:

[0165] A. Constructing the normalized matrix:

[0166]

[0167] B. Determine the ideal point A + and negative ideal point A - :

[0168]

[0169]

[0170] Among them, positive indicators are those with higher values, indicating a higher threat; negative indicators are those with higher values, indicating a lower threat. In this invention, indicators 2 and 5 are positive indicators, and indicators 1, 3, and 4 are negative indicators.

[0171] max 1≤1≤nIndex ij min 1≤1≤n Index ij These represent the maximum and minimum values ​​of the j-th indicator within the evaluation area, respectively.

[0172] C. Calculate A ij Distances to each positive and negative ideal point:

[0173]

[0174]

[0175] D. Calculate the relative closeness σ between the evaluation object and the ideal solution. i This represents the threat level of drone i.

[0176]

[0177] Step 3: Decision-making method for drone swarm countermeasures based on the performance of countermeasure equipment:

[0178] Typically, drone countermeasure systems define a protected area with a radius of 1 km, a denial zone with a radius of 3 km, and a warning zone with a radius of 5 km. Drones within the warning zone are detected and alerted, while countermeasure decisions are made for drones entering the denial zone. This method is effective for individual drones and struggles to make countermeasure decisions against drone swarm attacks. To achieve precise drone swarm countermeasure decisions, this invention proposes a drone swarm countermeasure decision-making method based on the performance of the countermeasure equipment, combining drone threat ranking results with the performance parameters of the countermeasure equipment. The specific steps are as follows:

[0179] 1. After the drone swarm enters the warning zone, the detection equipment detects and monitors the drone swarm, and ranks the drones in the swarm based on the monitoring results;

[0180] 2. Upon entering the denial zone, make a comprehensive decision based on the order of drone threats and the performance of various anti-soft kill countermeasures (such as electromagnetic interference, navigation interference, acoustic interference, and navigation deception).

[0181] 1) Let the distance between the drone and the control center point be d. UVA The minimum effective range of each countermeasure device is obtained as follows: and maximum effective range If countermeasures are available, they will meet the requirements. Then proceed to step 2); otherwise proceed to step 6.

[0182] 2) Combining the distance d between the drone and the control center point UVA and the relative flight speed v of the drone ′ Calculate the time t for the drone to fly to the control area.uva

[0183] t uva = UVA / ′

[0184] 3) Obtain the maximum response time {t1, ..., t2} for each device with distance constraints. n},like Then proceed to step 4); otherwise proceed to step 6.

[0185] 4) Obtain the device usage status that meets the distance and time constraints. If the device is not in use, and Then proceed to step 5); otherwise proceed to step 6.

[0186] 5) For countermeasures that meet the constraints of distance, time, and usage status, make countermeasure decisions in the order of acoustic interference, electromagnetic interference, navigation interference, and navigation deception. If the UAV cannot be successfully countered in the above order, proceed to step 6).

[0187] 6) If there are no countermeasures that meet the conditions, or if the soft-kill countermeasures fail, hard-kill methods such as lasers, missiles, and loitering munitions will be used.

[0188] The above content is readily understood by those skilled in the art. The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A countermeasure method for a UAV swarm attack, characterized in that, The method comprises: Collecting multi-source sensor detection data, obtaining target state data after time-space alignment, data association, track tracking filtering and target fusion on the pre-processed detection data, then performing target identification by using a standard database and a target identification feature library, combining the target state data to form final target state information; the multi-source sensor comprises a radar, a radio detection device and an optical detection device; According to the unmanned aerial vehicle type, the unmanned aerial vehicle relative speed, the unmanned aerial vehicle flight height, the distance between the unmanned aerial vehicle and the control area and the unmanned aerial vehicle communication network in the target state information, an unmanned aerial vehicle cluster threat evaluation index system is constructed, and the unmanned aerial vehicle cluster threat evaluation index system specifically comprises: Unmanned aerial vehicle type threat, based on the target state information, the size of the unmanned aerial vehicle volume is obtained, and the threat level is divided according to the unmanned aerial vehicle volume; the smaller the unmanned aerial vehicle volume, the higher the threat level; Relative speed threat of UAV, based on target state information to obtain the speed of the UAV relative to the control area : wherein, is the speed of the drone; is the angle of the drone relative to the center point of the prevention and control area; is the flight heading of the drone; speed The higher the threat level is, the higher the threat level is. Unmanned aerial vehicle flight height threat, based on the target state information, the unmanned aerial vehicle flight height is obtained, and the lower the flight height, the higher the threat level; Unmanned aerial vehicle distance threat, based on the target state information, the distance between the unmanned aerial vehicle and the control area center is obtained, and the smaller the distance, the higher the threat level; Clustered uav communication network uav criticality threat, uav based on target state information acquisition In clustered communication networks : wherein, denotes the shortest path number between the pair of UAV nodes and and denotes the total number of shortest paths between the pair of UAV nodes and and ; Criticality The higher, the higher the threat level; The unmanned aerial vehicle threat sorting is realized by using an entropy weight-based TOPSIS sorting method; the entropy weight-based TOPSIS sorting method specifically comprises the following steps: The five evaluation indexes in the unmanned aerial vehicle cluster threat evaluation index system are normalized; 5 wherein, represents the normalized first evaluation index of the i-th UAV in the cluster, represents the i-th evaluation index of the i-th UAV in the cluster, represents the number of UAVs in the UAV cluster;​​​​ calculating an entropy value of the first evaluation index and an entropy weight of the second evaluation index according to the normalization processing result ;​​​ 5 m Constructing a normalized matrix ; = ideal point and negative ideal point ; Among them, the positive type index is the larger the index value, the higher the threat; the negative type index is the larger the index value, the smaller the threat, and respectively represent the maximum value and the minimum value of the first evaluation index in the evaluation area. Calculations Distances to ideal points , Distances to negative ideal points ; Computing drone Threat level ; The countermeasure decision is made by combining the unmanned aerial vehicle threat sorting result and the countermeasure device performance parameter.

2. The method of claim 1, wherein, The collection of multi-source sensor detection data specifically comprises: When the radar does not detect the target and the radio detection device detects the target, the radio detection track is collected; When the radar detects the target and the radio detection device does not detect the target, the radar track is collected; When the radar and the radio detection device detect the target at the same time, the radar track is preferentially collected, and the unmanned aerial vehicle model and the frequency identified by the radio detection device are collected at the same time; When the radar and the optical detection device detect the target at the same time, the detection data of the radar and the optical detection device are collected at the same time.

3. The method according to claim 1 or 2, characterized in that, After the pre-processing of the detection data, the target state data is obtained through time-space alignment, data association, track tracking filtering and target fusion, and specifically comprises the following steps: The protocol and format conversion of the detection data are performed to form a unified data format; The outliers in the detection data are judged and removed; The detection data is compressed; The detection data is time calibrated and space calibrated; For the multi-target detection data collected by each sensor, the data association is performed based on the track association algorithm according to the sensor accuracy and the data dimension to determine which target the collected detection data belongs to; The track tracking filtering is performed on the target track by using the unscented Kalman filtering algorithm; The multi-sensor track information of the same target is fused to realize the tracking of the target.

4. The method of claim 1, wherein, The countermeasure decision is made by combining the unmanned aerial vehicle threat sorting result and the countermeasure device performance parameter, and specifically comprises: According to the unmanned aerial vehicle threat sorting result, the following operations are sequentially taken on each unmanned aerial vehicle from high to low in terms of unmanned aerial vehicle threat degree: (1) Set the distance between the UAV and the control center point as , obtain the minimum action distance and the maximum action distance of each soft-killing countermeasure equipment, if the countermeasure equipment satisfies , go to step (2), otherwise go to step (6); (2) combine the distance of the unmanned aerial vehicle from the control center point and the relative flight speed of the unmanned aerial vehicle to calculate the time for the unmanned aerial vehicle to fly to the control area : (3) Obtain the maximum response time of each device } If there is a countermeasure device , go to step (4), otherwise go to step (6); (4) Acquire countermeasure equipment The usage status; if the device is unused, and If yes, proceed to step (5); otherwise, proceed to step (6). (5) Anti-Device that meets distance, time and usage constraints , make countermeasures decision in the order of acoustic interference, electromagnetic interference, navigation interference and navigation deception, if the UAV is not countermeasured successfully in the above order, go to step (6); (6) The soft-killing countermeasure equipment fails to countermeasure, and the hard-killing countermeasure equipment is used to destroy the target unmanned aerial vehicle, wherein the hard-killing countermeasure equipment comprises a laser, a missile and a cruise missile.

5. A countermeasure system against a drone swarm attack, characterized by, The system comprises: a data acquisition and processing module, configured to acquire multi-source sensor detection data, perform time-space alignment, data correlation, track tracking filtering and target fusion on the preprocessed detection data to obtain target state data, and then perform target identification by using a standard database and a target identification feature library, combine the target state data, and form final target state information; the multi-source sensor comprises a radar, a radio detection device and an optical detection device; a threat ranking module, configured to construct an unmanned aerial vehicle cluster threat evaluation index system according to the unmanned aerial vehicle type, the relative speed of the unmanned aerial vehicle, the flight height of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the control area and the key nature of the communication network of the unmanned aerial vehicle cluster in the target state information, and construct the unmanned aerial vehicle cluster threat evaluation index system by the following units: an unmanned aerial vehicle type threat unit, configured to obtain the size of the unmanned aerial vehicle based on the target state information, divide the threat level according to the size of the unmanned aerial vehicle, and the smaller the size of the unmanned aerial vehicle, the higher the threat level; The unmanned aerial vehicle relative speed threat unit is used to obtain the speed of the unmanned aerial vehicle relative to the prevention and control region based on target state information : wherein, is the speed of the drone; is the angle of the drone relative to the center point of the containment zone; is the flight heading of the drone; speed The higher the threat level, the higher the threat level. an unmanned aerial vehicle flight height threat unit, configured to obtain the flight height of the unmanned aerial vehicle based on the target state information, and the lower the flight height, the higher the threat level; an unmanned aerial vehicle distance threat unit, configured to obtain the distance of the connecting line between the unmanned aerial vehicle and the center point of the control area based on the target state information, and the smaller the distance, the higher the threat level; The cluster unmanned aerial vehicle communication network unmanned aerial vehicle key threat unit is used to obtain unmanned aerial vehicles based on target state information In the cluster communication network : wherein, denotes the number of shortest paths between a pair of UAV nodes and and denotes the total number of shortest paths between a pair of UAV nodes and and ​ Criticality The higher, the higher the threat level; an unmanned aerial vehicle threat ranking unit, configured to realize unmanned aerial vehicle threat ranking by using an entropy weight-based TOPSIS ranking method; and a countermeasure decision module, configured to make a countermeasure decision in combination with the unmanned aerial vehicle threat ranking result and the performance parameters of the countermeasure equipment. 5 wherein, represents the normalized first evaluation index of the i-th UAV in the cluster, represents the normalized first evaluation index of the i-th UAV in the cluster, represents the normalized first evaluation index of the i-th UAV in the cluster, represents the normalized first evaluation index of the i-th UAV in the cluster, represents the normalized first evaluation index of the i-th UAV in the cluster, represents the normalized first evaluation index of the i-th UAV in the cluster, represents the number of UAVs in the UAV cluster; An entropy weight calculation unit is configured to calculate an entropy value of the first evaluation index and an entropy weight of the first evaluation index and the second evaluation index according to the normalization processing result . ;​​ 5 m a matrix calculation unit configured to construct a normalization matrix ; = ideal point calculation unit for determining an ideal point and a negative ideal point ; Among them, the positive type index is the larger the index value, the higher the threat; the negative type index is the larger the index value, the smaller the threat, and respectively represent the maximum value and the minimum value of the first evaluation index in the evaluation area. a distance calculation unit for calculating distances to ideal points , distances to negative ideal points ; a threat degree calculation unit configured to calculate a threat degree of the UAV ;​ The countermeasure decision module specifically comprises the following units executed in sequence:

6. The system of claim 5, wherein, according to the unmanned aerial vehicle threat ranking result, the units are executed on each unmanned aerial vehicle from high to low in terms of the unmanned aerial vehicle threat degree: a hard-killing countermeasure unit, configured to use the hard-killing countermeasure equipment to destroy the target unmanned aerial vehicle when the soft-killing countermeasure equipment fails to countermeasure, wherein the hard-killing countermeasure equipment comprises a laser, a missile and a cruise missile. The distance determination unit is configured to set the distance between the UAV and the control center point as , obtain the minimum action distance and the maximum action distance of each soft-killing countermeasure device, and if the countermeasure device satisfies , the speed determination unit is turned to, otherwise the hard-killing countermeasure unit is turned to. The speed determination unit is used to determine the distance between the drone and the control center point. and the relative flight speed of the drone Calculate the time it takes for the drone to fly to the control area. : A time judging module is configured to acquire the maximum response time of each device. If there is a countermeasure device , the state judging unit is entered, otherwise the hard-kill countermeasure unit is entered. A state judging unit is configured to acquire the usage state of the countermeasure device If the device is unused, and the soft-killing countermeasure unit is entered, otherwise the hard-killing countermeasure unit is entered. Soft-kill countermeasure unit for countermeasure equipment meeting distance, time and use state constraints , making countermeasure decisions in the order of acoustic jamming, electromagnetic jamming, navigation jamming and navigation deception, and if the UAV is not successfully countermeasured in the above order, going to the hard-kill countermeasure unit; ​

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