Unmanned aerial vehicle countering method and system based on detection sensor

Abstract

The invention discloses an unmanned aerial vehicle countering method and system based on a detection sensor, and the method comprises the steps: determining a countering signal and a countering range according to an air position model, a current working mode of a detection unmanned aerial vehicle and a flight path of an invading unmanned aerial vehicle, and achieving the visual detection of the detection unmanned aerial vehicle for the invading unmanned aerial vehicle. And the countering effect of the detection unmanned aerial vehicle on the invading unmanned aerial vehicle is ensured. If the invading unmanned aerial vehicle leaves the countering range, triggering a follow-up countering mode of the detection unmanned aerial vehicle relative to the invading unmanned aerial vehicle according to the departure direction of the invading unmanned aerial vehicle; the detection unmanned aerial vehicle captures the falling track of the invading unmanned aerial vehicle based on visual detection, determines the early warning aperture according to the falling track of the invading unmanned aerial vehicle and the orientation of the lamp body of the detection unmanned aerial vehicle, realizes follow-up countering of the detection unmanned aerial vehicle relative to the invading unmanned aerial vehicle, and controls the falling position of the invading unmanned aerial vehicle based on visual detection. And dynamic early warning is carried out through the early warning aperture.

Description

Technical Field

[0001] The present invention relates to the technical field of drone countermeasure methods, and in particular to a drone countermeasure method and system based on a detection sensor. Background Art

[0002] With the development of science and technology, drones are used as components for air patrols. At this time, detection drones are generally used as patrol prototypes and patrol in the air. Invasion drones are used as drones to be controlled and are subject to countermeasures by detection drones. In existing technologies, detection drones perform air countermeasures based on the real-time position of the invading drones, and do not take into account the flight trajectory of the invading drones, thereby reducing the countermeasure effect of detection drones on invading drones. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method and system for countering drones based on detection sensors.

[0004] An embodiment of the present invention provides a method for countering drones based on a detection sensor, comprising:

[0005] determining aerial imagery based on visual detection by a detection drone;

[0006] Determine an aerial position model of the detection UAV relative to the intrusion UAV based on the aerial image and the distance data detected by the detection sensor;

[0007] Determine the countermeasure signal and countermeasure range based on the aerial position model, the current working mode of the detection drone, and the flight trajectory of the intruding drone;

[0008] If the invading drone leaves the countermeasure range, the detection drone will be triggered to follow the invading drone in a countermeasure mode according to the invading drone's departure direction until the invading drone is in a disabled state;

[0009] The detection drone captures the falling trajectory of the intruding drone based on visual detection, and determines the warning aperture according to the falling trajectory of the intruding drone and the direction of the detection drone's light body.

[0010] An embodiment of the present invention provides a detection sensor-based drone countermeasure system, which is applied to the above-mentioned detection sensor-based drone countermeasure method. The detection sensor-based drone countermeasure system includes:

[0011] an aerial image module for determining aerial imagery based on visual detection by a detection drone;

[0012] An aerial position module is used to determine an aerial position model of the detection UAV relative to the intrusion UAV based on the aerial image and the distance data detected by the detection sensor;

[0013] The countermeasure module is used to determine the countermeasure signal and countermeasure range based on the aerial position model, the current working mode of the detection drone, and the flight trajectory of the intruding drone;

[0014] The follow-up module is used to trigger the follow-up countermeasure mode of the detection drone relative to the invading drone according to the escape direction of the invading drone if the invading drone leaves the countermeasure range until the invading drone is in a failure state;

[0015] The early warning aperture module is used to detect drones and capture the falling trajectory of intruding drones based on visual detection. The early warning aperture is determined according to the falling trajectory of the intruding drone and the direction of the light body of the detection drone.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] In an embodiment of the present invention, through the method in the embodiment of the present invention, an aerial image is determined based on the visual detection of the detection drone; the aerial position model of the detection drone relative to the intrusion drone is determined based on the aerial image and the distance data detected by the detection sensor; the counter signal and the counter range are determined based on the aerial position model, the current working mode of the detection drone and the flight trajectory of the intrusion drone, which is compatible with the overall consideration of the counter signal and the counter range, and covers the flight trajectory of the intrusion drone, realizes the visual detection of the intrusion drone by the detection drone, and ensures the counter effect of the detection drone on the intrusion drone.

[0018] Therefore, if the invading drone leaves the countermeasure range, the detection drone will trigger a follow-up countermeasure mode relative to the invading drone according to the departure direction of the invading drone until the invading drone is in a failure state; the detection drone captures the falling trajectory of the invading drone based on visual detection, and determines the early warning aperture according to the falling trajectory of the invading drone and the direction of the detection drone's light body, thereby realizing the follow-up countermeasure of the detection drone relative to the invading drone and ensuring the dynamic countermeasure effect of the detection drone. At the same time, the detection drone controls the falling position of the invading drone based on visual detection, and issues dynamic early warnings through the early warning aperture. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the UAV countermeasure method based on detection sensors in the present invention;

[0020] Figure 2 1 is a flow chart of step S11 in the drone countermeasure method based on detection sensors in the present invention;

[0021] Figure 3 1 is a flow chart of step S12 in the drone countermeasure method based on detection sensors in the present invention;

[0022] Figure 4 1 is a flow chart of step S13 in the drone countermeasure method based on detection sensors in the present invention;

[0023] Figure 5 1 is a flow chart of step S14 in the method for countering drones based on detection sensors in the present invention;

[0024] Figure 6 1 is a flow chart of step S15 in the drone countermeasure method based on detection sensors in the present invention;

[0025] Figure 7 It is a schematic diagram of the structural composition of the UAV countermeasure system based on detection sensors in the present invention. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] Example 1:

[0028] See also Figures 1 to 7 This embodiment provides a drone countermeasure method based on a detection sensor, which is applied to a drone countermeasure scenario based on a detection sensor. The drone countermeasure method includes:

[0029] Step S11: Acquire an aerial image according to the visual imaging system of the detection drone;

[0030] Step S12: determining an aerial position model of the detection UAV relative to the intrusion UAV based on the aerial image and the distance data detected by the detection sensor;

[0031] Step S13: Determine a countermeasure signal and a countermeasure range based on the aerial position model, the current operating mode of the detection drone, and the flight trajectory of the intruding drone;

[0032] Step S14: If the invading drone leaves the countermeasure range, triggering the detection drone's follow-up countermeasure mode relative to the invading drone according to the invading drone's departure direction until the invading drone is in a malfunction state;

[0033] Step S15: The detection drone captures the falling trajectory of the intruding drone based on the visual detection system, and determines the warning aperture according to the falling trajectory of the intruding drone and the direction of the lighting device carried by the detection drone.

[0034] refer to Figure 2In step S11, the aerial image is determined based on the visual detection of the detection drone, including the following steps:

[0035] S111: During the flight of the detection drone, determining the imaging parameters of the plurality of cameras according to the flight speed of the detection drone, environmental parameters of the environment in which the detection drone is located, and a mapping relationship of imaging parameters; the plurality of cameras are mounted on the detection drone and are mounted at different positions of the detection drone, such as the front, rear, top, bottom, left, and right of the detection drone body, to provide a full range of imaging fields;

[0036] S112: Based on the shooting parameters of multiple cameras, multiple sub-images centered on the detection drone are obtained, and the multiple sub-images correspond to images in different directions of the detection drone body (such as one or more of the front, back, top, bottom, left, and right of the detection drone body), and the aerial image is obtained by stitching the multiple sub-images.

[0037] During the flight of the detection UAV, the flight speed of the detection UAV, the environmental parameters of the detection UAV's environment, and the mapping relationship of the camera parameters will affect the imaging quality of the camera.

[0038] For example, the flight control system adjusts the camera's exposure time based on flight speed and environmental parameters. If the flight speed is too fast, the exposure time needs to be shortened accordingly to prevent image blur. At the same time, the frame rate also needs to be increased to ensure continuous, clear images are captured. At the same time, the drone's flight control system monitors the flight speed in real time and automatically adjusts the camera's parameters based on a preset mapping relationship, where the camera's imaging parameters include the camera's exposure time.

[0039] Assume that the detection drone is equipped with four cameras, located in the front, back, left and right directions of the drone, and is passing through an area with thick clouds, resulting in dim light when the camera images. At this time, the flight control system of the detection drone detects its flight speed in real time and automatically adjusts the angle of the camera gimbal and the camera parameters according to the flight speed. For example, if the flight speed is low, the drone takes a long time to pass through the cloud area. At this time, the flight control system automatically adjusts the gimbal angle within a range of 0-10°, shortens the camera exposure time to 1 / 1000 second, increases the frame rate to 60 frames / second, increases the camera gain, and adjusts the contrast and sharpness to ensure that clear images are captured.

[0040] Furthermore, the camera parameter mapping relationship refers to the correspondence between the camera configuration (such as position, lens orientation angle, focal length) and the camera parameters (such as exposure time, frame rate, gain, contrast, etc.). This mapping relationship can be determined through experiments during the UAV design and testing phase and stored in the UAV's flight control system; when the UAV is flying, the flight control system also automatically selects the corresponding camera parameters according to the current configuration of the camera (such as the rotation angle of the mechanical gimbal and the focal length of the camera).

[0041] For example, if a camera is adjusted to a bird's-eye view, the flight control system will select parameters such as focal length and exposure time suitable for the bird's-eye view; the images obtained by the above camera are processed and synthesized in real time by the drone's flight control system to form a full-scale aerial image, providing key information for subsequent countermeasure operations. As a result, the detection drone can automatically adjust the camera parameters in complex aerial environments, capture high-quality images, and provide strong support for subsequent countermeasure missions.

[0042] At this time, during the flight of the detection drone, its flight control system will trigger the visual detection function according to the previously determined camera parameters of each camera (such as exposure time, frame rate, gain, etc.), which means that the camera will start capturing images, and the above images will be optimized based on the preset parameters; at the same time, the flight control system will send instructions to each camera to start the image capture function; the camera will start capturing images according to the received parameter settings.

[0043] Optionally, assume that the detection drone is performing an aerial patrol mission, and its four cameras are located in the front, rear, left and right respectively; at this time, the drone is flying over an urban area with many high-rise buildings and roads around it; the flight control system triggers the visual detection function of the four cameras according to the previously determined camera parameters (such as exposure time of 1 / 500 second, frame rate of 30 frames / second, etc.).

[0044] The flight control system receives sub-images from each camera and uses image stitching or fusion algorithms to combine the sub-images into a complete aerial image; the aerial image will provide a comprehensive view of the drone's surroundings; at the same time, the image stitching algorithm will consider factors such as overlapping areas, perspective differences and image distortion between cameras to ensure that the synthesized image is accurate and seamless; the fusion algorithm focuses more on improving the contrast and clarity of the image, and reducing noise and artifacts.

[0045] Optionally, the flight control system receives four sub-images and uses an image stitching algorithm to synthesize the sub-images into a complete aerial image; the aerial image shows the 360-degree environment around the drone, including the skyline in front, the tail view behind, and the buildings and streets on the left and right; through the synthesized image, obstacles, roads and potential targets around the drone can be clearly seen; thus, the detection drone can use the sub-images captured by multiple cameras to synthesize a comprehensive aerial image, providing key information for subsequent countermeasure operations, which greatly improves the drone's perception and safety in complex environments.

[0046] In one embodiment of the present application, it is assumed that the detection drone is equipped with four cameras, located at the front, rear, left, and right sides respectively; the flight control system has a camera matching table, which is shown in Table 1:

[0047] Table 1 Camera matching table

[0048] Camera number direction Camera 1 ahead Camera 2 rear Camera 3 Left side Camera 4 right side

[0049] When the drone patrols in the air, four cameras start capturing images, and then the flight control system synthesizes the sub-images into a complete aerial image based on an image stitching algorithm.

[0050] refer to Figure 3 In step S12, the aerial position model of the detection UAV relative to the intrusion UAV is determined based on the aerial image and the distance data detected by the detection sensor, which specifically includes the following steps:

[0051] S121: Determine the shape of the invading drone based on the aerial image, and determine the direction of the detection drone relative to the invading drone based on the shape of the invading drone, the position of the invading drone, and the position of the detection drone, and adjust the flight direction of the detection drone along the direction so that the head of the detection drone faces the invading drone;

[0052] S122: A detection sensor is disposed in front of the detection drone and is used to obtain distance data between the detection drone and the intruding drone, and determine an aerial position model of the detection drone relative to the intruding drone based on the distance data, the position of the detection drone, and the position of the intruding drone;

[0053] The morphology of the intruding drone can be determined by the visual detection system of the detection drone. For example, the visual detection system of the detection drone can use an image recognition algorithm (such as a convolutional neural network (CNN)) to identify the intruding drone in the aerial image and obtain the morphological parameters of the intruding drone based on the recognition results. This process includes steps such as feature extraction, target classification, and bounding box positioning; the morphological parameters of the intruding drone include one or more of the size of the drone (which can be estimated based on the size of the target detection box), shape (such as a quadcopter drone), and model.

[0054] After identifying an intruding drone, the visual detection system calculates the position coordinates of the intruding drone in three-dimensional space through geometric transformation based on the pixel coordinates in the image and the actual size of the intruding drone (obtained through a preset database or previous recognition experience), combined with parameters such as the drone's flight altitude and the camera's field of view; the position coordinates of the intruding drone are relative to the current position of the detection drone.

[0055] With the location coordinates of the intruding drone and the detection drone (which can be obtained through GPS or other navigation devices), the flight control system calculates the relative position between the two, which includes at least one of the heading angle, pitch angle, and roll angle.

[0056] Once the relative position is determined, the flight control system sends instructions to the drone's flight control system to adjust the drone's heading and speed (and altitude) so that it flies toward the invading drone. This process requires real-time updates and adjustments as the position and speed of the invading drone change. The flight control system uses an autopilot or flight algorithm to ensure that the drone can fly smoothly and accurately in the predetermined direction.

[0057] Based on the pixel coordinates in the image and the actual size of the invading drone, combined with parameters such as the drone's flight altitude and the camera's field of view, the system calculated that the invading drone was approximately 300 meters northeast of the detection drone; based on the position coordinates of the two drones, the flight control system calculated that the detection drone needed to turn approximately 45 degrees to the northeast in order to face the invading drone; after receiving the command, the flight control system began to adjust the heading and speed of the detection drone, causing it to gradually turn to the northeast and gradually approach the invading drone; during the flight, the flight control system continuously monitored and adjusted the flight parameters to ensure that the drone could fly smoothly and accurately in the predetermined direction; thus, the detection drone could flexibly adjust its flight direction to respond to changes in the position and shape of the invading drone, thereby achieving effective surveillance and tracking.

[0058] Furthermore, at this time, one or more types of detection sensors are usually configured in front of the detection drone, such as laser radar (LiDAR), infrared sensors, radar systems, etc.; the specific types of the above sensors depend on the mission requirements and the design of the drone; for this step, the focus is on obtaining the straight-line distance between the detection drone and the intrusion drone.

[0059] Optionally, assume that the detection drone detects the presence of an intruding drone through a lidar sensor in front of it while performing an aerial surveillance mission; the lidar sensor is configured in front of the detection drone, and it actively emits laser pulses and receives reflected signals.

[0060] The detection sensor converts raw data (such as the reflection time of laser pulses, the intensity of infrared radiation, etc.) into distance data, which usually involves signal processing and algorithm calculation to extract useful distance information; the final distance data represents the straight-line distance between the detection drone and the intrusion drone, which can be used as the basis for subsequent calculation of the aerial position model.

[0061] In addition to distance data, the specific location information of the detection drone and the intrusion drone is also needed, which is obtained through the GPS system, inertial navigation system (INS) or other positioning technologies; at the same time, using the principles of geometry and spatial analytic geometry, combined with distance data and location information, the aerial position model of the detection drone relative to the intrusion drone is calculated; the aerial position model usually includes information such as relative distance and relative position (such as azimuth and pitch angle).

[0062] Furthermore, by using information and geometric principles, the aerial position model of the detection drone relative to the intrusion drone can be calculated; for example, it is calculated that the detection drone needs to fly at an angle of about 45 degrees to the northeast to approach the intrusion drone and maintain a relative distance of about 300 meters (taking into account the movement and errors of the drone, the relative distance will be dynamically adjusted).

[0063] As time goes by, both the detection drone and the intrusion drone will move, so the aerial position model needs to be constantly updated; the lidar sensor will continuously detect, and the GPS system will continuously update the position information to ensure the accuracy and real-time nature of the aerial position model; thus, the detection drone can accurately determine its relative position to the intrusion drone, providing strong support for subsequent mission execution (such as tracking, surveillance, countermeasures, etc.).

[0064] refer to Figure 4 In step S13, the countermeasure signal and countermeasure range are determined according to the aerial position model, the current working mode of the detection drone, and the flight trajectory of the intruding drone. The specific steps are as follows:

[0065] S131: In the aerial position model, the relative distance between the detection UAV and the intruder UAV is updated in real time. If the relative distance is greater than a preset relative distance threshold, the flight speed of the detection UAV is adjusted based on the flight speed and relative distance of the intruder UAV so that the relative distance is dynamically maintained within the preset relative distance threshold.

[0066] S132: collecting the current working mode of the detection drone, detecting the flight position of the intruding drone based on the camera vision of the detection drone, and determining the flight trajectory of the intruding drone based on the synthesis of multiple flight positions;

[0067] S133: Determine the first mode coefficient according to the aerial position model and the current working mode of the detection drone, determine the second mode coefficient according to the aerial position model and the flight trajectory of the invading drone, and determine the corresponding counter mode according to the mapping relationship between the first mode coefficient, the second mode coefficient and the counter mode. The counter mode covers the corresponding counter signal and counter range.

[0068] In an embodiment of the present application, in the aerial position model, the relative distance between the detection drone and the intrusion drone is updated in real time. If the relative distance is greater than a preset relative distance threshold, the flight speed of the detection drone is adjusted according to the flight speed and relative distance of the intrusion drone, so that the relative distance is dynamically maintained within the preset relative distance threshold, thereby ensuring that the detection drone tracks the intrusion drone.

[0069] At this point, an aerial position model has been established between the detection UAV and the intrusion UAV, which contains the relative position information between the two; based on the aerial position model, the relative distance between the detection UAV and the intrusion UAV is calculated in real time.

[0070] A relative distance threshold is set based on mission requirements and safety considerations; the threshold represents the maximum distance allowed between the detection UAV and the intrusion UAV; the real-time calculated relative distance is compared with the preset threshold; if the relative distance is greater than the threshold, it means that the detection UAV needs to adjust its flight speed to approach the intrusion UAV.

[0071] Based on the flight speed and relative distance of the intruding drone, the flight speed of the detection drone that needs to be adjusted is calculated, which usually involves the synthesis and decomposition of the velocity vector, as well as the calculation of acceleration and deceleration; the calculated flight speed is sent to the flight control system of the detection drone to adjust the speed of the drone. Since the flight speed and direction of the intruding drone will change, the flight speed of the detection drone also needs to be dynamically adjusted.

[0072] Furthermore, the current working mode of the detection drone is collected, the flight position of the invading drone is detected based on the camera vision of the detection drone, and the flight trajectory of the invading drone is determined based on the synthesis of multiple flight positions, ensuring the accuracy of the flight trajectory of the invading drone.

[0073] At this time, the detection drone has multiple working modes, such as tracking, surveillance, patrol, counterattack, etc.; each working mode corresponds to different mission requirements and flight strategies; at the same time, information on the current working mode is usually stored in the drone's flight management system, or obtained through communication between the drone and the ground control station; the collection process involves reading stored data, parsing communication protocols, etc.; understanding the current working mode is crucial for subsequent mission execution because it can determine how the drone responds to the dynamic changes of the intruding drone.

[0074] Detection drones are usually equipped with high-definition cameras to capture images of the surrounding environment in real time. Computer vision algorithms are used to process the images captured by the cameras to identify and locate the flight position of the intruding drone, which involves steps such as target detection, tracking, and feature extraction. The visual detection algorithm outputs the real-time location data of the intruding drone, including two-dimensional image coordinates or three-dimensional space coordinates.

[0075] The continuously captured flight position data of invading drones are synthesized to form a time series position data set; the position data set is processed using trajectory calculation algorithms (such as Kalman filtering, particle filtering, etc.) to smooth noise, fill missing data, and estimate the actual flight trajectory of the invading drone; finally, the flight trajectory of the invading drone is output. The flight trajectory can be displayed in two-dimensional or three-dimensional graphical form, or provided to subsequent processing modules in the form of numerical data.

[0076] Therefore, the first mode coefficient is determined according to the aerial position model and the current working mode of the detection drone, the second mode coefficient is determined according to the aerial position model and the flight trajectory of the invading drone, and the corresponding counter mode is determined according to the mapping relationship between the first mode coefficient, the second mode coefficient and the counter mode. The counter mode covers the corresponding counter signal and counter range, and is compatible with the overall consideration of the first mode coefficient, the second mode coefficient and the counter mode mapping relationship, thereby ensuring the accuracy of the corresponding counter mode.

[0077] At this time, the aerial position model contains information such as the relative position, speed, acceleration, etc. between the detection drone and the intrusion drone; at the same time, the detection drone is in different working modes such as tracking, monitoring, interception, and counterattack; combining the aerial position model and the current working mode, the first mode coefficient is calculated through a preset algorithm or lookup table; the first mode coefficient reflects the specific state or capability of the detection drone in the current position and mode.

[0078] If the detected drone is currently in "interception mode" and according to the air position model, it is rapidly approaching the invading drone, with a good speed match and an obvious position advantage, the first mode coefficient is calculated to be 0.8 (indicating that the current state is good and close to the optimal interception conditions).

[0079] For the second mode coefficient, the flight trajectory of the invading drone has been determined through previous steps (such as S133); the flight trajectory is analyzed to extract key features, such as speed changes, direction changes, flight altitude, etc.; based on the trajectory features and the aerial position model, the second mode coefficient is calculated through a preset algorithm, and the second mode coefficient reflects the dynamic behavior or potential threat of the invading drone.

[0080] For example, by analyzing the flight trajectory of an invading drone, it was found that its speed gradually slowed down, its direction deviated, and its flight altitude decreased. These characteristics indicate that the invading drone is performing evasive action or encountering a malfunction, so the second mode coefficient is calculated to be 0.6 (indicating that the threat of the invading drone has decreased, but vigilance is still required).

[0081] The countermeasure mode mapping relationship is a preset mapping table or algorithm, which maps the first mode coefficient and the second mode coefficient to a specific countermeasure mode; based on the calculated first mode coefficient and the second mode coefficient, the corresponding countermeasure mode is searched or calculated in the countermeasure mode mapping relationship; the countermeasure mode usually includes a countermeasure signal (such as an interference signal, an interception signal, etc.) and a countermeasure range (such as an interference radius, an interception area, etc.). The above parameters determine how to effectively counter the invading drone.

[0082] Optionally, according to the first mode coefficient (0.8) and the second mode coefficient (0.6), the corresponding counter mode is searched or calculated in the counter mode mapping relationship; assuming that the mapping relationship is a linear combination, that is, the counter mode strength is proportional to the weighted average of the two mode coefficients; the calculated counter mode is "medium intensity interference + local interception area", where the interference signal intensity is medium, and the interception area is centered on the detection drone with a radius of 200 meters; thus, the detection drone can dynamically adjust the counter strategy according to the current working mode, its relative position to the invading drone, and the flight trajectory of the invading drone to ensure the effective execution of the mission.

[0083] In this embodiment, a countermeasure pattern matching table is collected, and the countermeasure pattern matching table is shown in Table 2:

[0084] Table 2 Countermeasure pattern matching table

[0085]

[0086] Assume that the first mode coefficient is "medium" (indicating that the detection drone is in a medium-effective state), and the second mode coefficient is "high" (indicating that the invading drone poses a high threat); according to the countermeasure mode matching table, the corresponding countermeasure mode is "induced interception + information theft", the countermeasure signal type is "laser interference", and the countermeasure range is "specific direction area".

[0087] refer to Figure 5 In step S14, if the invading drone leaves the countermeasure range, the detection drone is triggered to follow the countermeasure mode relative to the invading drone according to the departure direction of the invading drone until the invading drone is in a failure state;

[0088] In the specific implementation process of the present invention, the specific steps are:

[0089] S141: When the invading drone is countered by the detection drone, the flight speed of the invading drone is further increased to escape the countering range. At this time, the escape direction of the invading drone is collected based on the visual detection of the detection drone.

[0090] S142: Determining a direction adjustment instruction for the detection drone based on the departure orientation of the intruding drone and the flight direction of the detection drone, and triggering a flight turn of the detection drone based on the direction adjustment instruction so that the detection drone continues to face the intruding drone, and triggering a follow-up countermeasure mode of the detection drone relative to the intruding drone;

[0091] S143: In this follow-up countermeasure mode, the detection drone performs follow-up flight relative to the intrusion drone and further expands the countermeasure range according to the parameter adjustment of the detection drone until the intrusion drone is successfully countered by the detection drone and the intrusion drone is in a failure state.

[0092] In an embodiment of the present application, when the invading drone is countered by the detection drone, the flight speed of the invading drone is further increased to escape the counterattack range. At this time, the escape direction of the invading drone is collected based on the visual detection of the detection drone, and the escape direction of the invading drone is introduced.

[0093] At this time: during the interaction between the detection drone and the intrusion drone, the detection drone has implemented countermeasures against the intrusion drone in some way (such as interference signals, interception devices, etc.); the countermeasures have affected the flight stability of the intrusion drone, or its communication system and navigation system have been interfered with.

[0094] Faced with the counterattack of the detection drone, the invading drone will take countermeasures, one of which is to increase the flight speed in an attempt to escape the counterattack range of the detection drone; the speed increase of the invading drone is sudden or gradual, which depends on its flight control system and current power status.

[0095] Real-time detection and analysis of the surrounding environment; when the intruding drone begins to accelerate and try to escape the countermeasure range, the visual detection system of the detection drone can capture this change and calculate the escape direction of the intruding drone relative to the detection drone; the escape direction usually includes direction (such as east, south, west, north or a specific angle) and distance (such as how many meters or kilometers); the visual detection system will continuously update the position and escape direction of the intruding drone to ensure that the detection drone can track its dynamics in real time.

[0096] Furthermore, the direction adjustment instruction of the detection drone is determined according to the separation position of the invading drone and the flight direction of the detection drone, and the flight turning of the detection drone is triggered according to the direction adjustment instruction, so that the detection drone continues to face the invading drone, and the follow-up countermeasure mode of the detection drone relative to the invading drone is triggered, which is compatible with the overall consideration of the separation position of the invading drone and the flight direction of the detection drone, and ensures the accuracy of the direction adjustment instruction of the detection drone.

[0097] At this time, the input information includes the escape direction of the invading drone (provided by step S141) and the current flight direction of the detection drone; the flight management system of the detection drone will combine the above information and calculate the direction adjustment instruction through a preset algorithm or logic; the direction adjustment instruction is intended to enable the detection drone to adjust its flight direction so that it continues to face the invading drone; the direction adjustment instruction usually includes information such as the adjustment angle, rate, and required flight altitude or speed adjustment.

[0098] For example, when a detection drone is performing a countermeasure mission, it has successfully locked onto an intruding drone and implemented preliminary countermeasures; however, the intruding drone suddenly accelerates and attempts to change its flight direction to escape the countermeasure range; the flight management system of the detection drone detects that the intruding drone is accelerating toward the northwest, while the detection drone is currently facing north, so the system calculates that the flight direction needs to be adjusted approximately 45 degrees to the west in order to continue to face the intruding drone.

[0099] The flight control system of the detection drone is responsible for executing direction adjustment instructions, which usually involve actuators such as the drone's rudder, engine thrust distribution or vector thrust system; according to the direction adjustment instructions, the flight control system will trigger the corresponding steering operation; during the steering process, the flight control system will continuously receive real-time feedback from various sensors on the drone to ensure the accuracy and stability of the steering operation.

[0100] Once the detection drone successfully adjusts its flight direction and continues to face the intruder drone, its flight management system will automatically trigger the follow-up countermeasure mode; the follow-up countermeasure mode is a dynamically adjusted countermeasure strategy that allows the detection drone to dynamically adjust its countermeasures based on the real-time location and dynamics of the intruder drone; in the follow-up countermeasure mode, the detection drone will take a series of countermeasure actions, such as emitting jamming signals, activating interception devices, or executing other preset countermeasures.

[0101] Optionally, once the detection drone successfully adjusts its flight direction and continues to face the intruding drone, its flight management system automatically switches to follow-up countermeasure mode; in follow-up countermeasure mode, the detection drone begins to transmit interference signals in an attempt to interfere with the communication and navigation systems of the intruding drone; at the same time, the system also continuously analyzes the flight trajectory and speed changes of the intruding drone in order to adjust the intensity and frequency of the interference signal as needed; thus, the detection drone can flexibly respond to the dynamic changes of the intruding drone and always maintain an effective countermeasure posture, which helps to ensure the successful execution of the countermeasure mission and minimize the impact on the surrounding environment and personnel.

[0102] Therefore, in this follow-up countermeasure mode, the detection drone follows the intruder drone and further expands the countermeasure range according to the parameter adjustment of the detection drone until the intruder drone is successfully countered by the detection drone and the intruder drone is in a failure state;

[0103] At this time, follow-up flight means that the detection drone dynamically adjusts its flight trajectory and speed according to the real-time position and dynamics of the invading drone to keep close tracking of the invading drone. It involves the flight control system of the detection drone, which will continuously receive data from various sensors on the drone (such as GPS position, speed, altitude, attitude, etc.), and combine the position and dynamic information of the invading drone (such as obtained through visual detection or radar system) to calculate and adjust the flight parameters of the drone. The goal of follow-up flight is to ensure that the detection drone can always remain within the effective countermeasure range of the invading drone, no matter how the invading drone changes its flight trajectory or speed.

[0104] Optionally, assume that the detection drone successfully locks onto an unauthorized intrusion drone while performing an air interception mission; in order to force the intrusion drone to land or crash, the detection drone enters a follow-up countermeasure mode; as the intrusion drone begins to perform complex maneuvers to try to get rid of tracking, the flight control system of the detection drone continuously adjusts its flight parameters to maintain close tracking; for example, when the intrusion drone suddenly climbs, the detection drone also quickly increases thrust and adjusts its attitude to follow its upward trajectory.

[0105] During the follow-up flight process, the flight control system of the detection drone will adjust various flight parameters of the drone as needed, such as speed, altitude, attitude, etc., in order to optimize the tracking performance and counter-attack effect of the drone; as the detection drone tracks the invading drone more and more closely, the system will gradually increase the intensity or range of the counter-attack measures according to the preset strategy or algorithm; for example, if the invading drone attempts to get rid of the counter-attack through high-speed maneuvers, the detection drone will increase the power or range of the interference signal to ensure continuous and effective counter-attack; parameter adjustment and counter-attack range expansion is a dynamic process, which needs to be continuously optimized and adjusted according to the real-time response of the invading drone and the current status of the detection drone.

[0106] The standard for a successful countermeasure is usually that the invading drone is unable to continue to perform its original mission or maintain a stable flight state, which is manifested by interference with the invading drone's communication system, failure of the navigation system, failure of the power system, etc.; once the invading drone reaches a failure state, it will lose control, crash or be forced to land, which depends on the type and intensity of the countermeasures of the detection drone, as well as the design and performance of the invading drone; after a successful countermeasure, the detection drone will perform a series of subsequent processing operations, such as recording data from the countermeasure process, reporting the countermeasure results to the command center, and continuing to monitor the area.

[0107] Optionally, after a period of follow-up flight and implementation of countermeasures, the communication system of the invading drone is severely interfered with and the navigation system begins to fail; eventually, the invading drone loses control, begins to fly unstably and eventually crashes in an open area; after confirming that the countermeasure is successful, the detection drone records relevant data and reports the countermeasure results to the command center; thus, the detection drone can effectively track and counter the invading drone in a complex and changeable aerial environment, thereby ensuring airspace safety and successful mission execution.

[0108] In one embodiment of the present application, a stage matching table is used to show how the detection drone adjusts its own parameters and countermeasure strategies according to the status of the intrusion drone at different stages; the stage matching table is shown in Table 3:

[0109] Table 3 Stage Matching Table

[0110]

[0111] refer to Figure 6 In step S15, the detection drone captures the falling trajectory of the intruding drone based on visual detection, and determines the warning aperture according to the falling trajectory of the intruding drone and the direction of the light body of the detection drone;

[0112] In the specific implementation process of the present invention, the specific steps are:

[0113] S151: After the intruder drone is in a malfunctioning state, it descends and is unable to fly. The detection drone captures multiple landing locations of the intruder drone based on visual detection, and determines the falling trajectory of the intruder drone based on the multiple landing locations, the current wind direction, and the weight of the intruder drone.

[0114] S152: Predicting the drop position of the intruding drone relative to the ground based on the drop trajectory of the intruding drone, and determining an orientation instruction for the lamp based on the drop position, the posture of the detection drone, and the orientation of the lamp configured on the detection drone;

[0115] S153: Determine a warning aperture according to the directional instruction of the lamp body, the light-emitting range of the lamp body, and the light-emitting mode of the lamp body, and the falling position is within the warning aperture.

[0116] In an embodiment of the present application, after the invading drone is in a malfunctioning state, the invading drone descends and cannot fly. The detection drone captures multiple landing positions of the invading drone based on visual detection, and determines the falling trajectory of the invading drone based on the multiple landing positions, the wind direction of the current environment, and the weight of the invading drone. It takes into account the overall consideration of multiple landing positions, the wind direction of the current environment, and the weight of the invading drone to ensure the accuracy of the falling trajectory of the invading drone.

[0117] At this time, after the invading drone is in a malfunctioning state due to the counterattack of the detection drone, it will begin to descend; at this time, the detection drone needs to capture the multiple landing positions of the invading drone based on its visual detection system, and comprehensively consider multiple factors (including multiple landing positions, the wind direction of the current environment, and the weight of the invading drone) to determine the final falling trajectory of the invading drone.

[0118] At the same time, the detection drone's visual detection system (such as a camera) will capture images of the intruding drone in real time and identify its position in the air; as the intruding drone descends, the detection drone will record its multiple landing locations at different time points. The above position data can be used as the basis for subsequent analysis.

[0119] Optionally, assume that the detection drone successfully locks onto the intruder drone while performing a countermeasure mission and forces it into a disabled state; at this time, the intruder drone begins to descend from an altitude of 100 meters to the ground; the visual detection system of the detection drone captures multiple positions of the intruder drone in the air and records them; for example, during the descent, the intruder drone moves from its initial position (100 meters altitude, X = 100 meters, Y = 200 meters) to position A (80 meters altitude, X = 98 meters, Y = 198 meters), and then to position B (60 meters altitude, X = 96 meters, Y = 195 meters), and so on.

[0120] The detection drone is equipped with a meteorological sensor to detect the wind direction and speed of the current environment in real time. If there is no meteorological sensor, the system also obtains the above information from other sources (such as weather station data). The wind direction will have a significant impact on the drop trajectory of the intruding drone. For example, if the wind direction is north, the intruding drone will drop slightly to the south. The system needs to consider the wind direction and wind speed comprehensively to calculate the offset.

[0121] The detection drone knows the intruder's model and weight in advance, or identifies its model through a visual inspection system and queries the corresponding weight data from a database. The weight of the intruder drone affects its impact on wind and gravity. Heavier drones are less affected by wind during a fall, while lighter drones are more easily blown by the wind. The system performs a comprehensive calculation based on the aforementioned factors (multiple landing locations, wind direction, and weight) to determine the intruder's final drop trajectory. Predicting the final drop trajectory is a dynamic process, as the intruder's position and speed will change as it descends. The detection drone needs to continuously update its predictions.

[0122] Optionally, the detection drone identifies the model of the invading drone as XX-123 through a visual detection system, and finds from the database that the weight of the invading drone of this model is 5 kilograms; after system analysis, it is believed that the weight of the invading drone will be affected by a small wind force during the falling process. Combining the above factors, the detection drone predicts the final falling trajectory of the invading drone; for example, the system believes that the invading drone will start from the initial position, slightly deviate to the west (affected by the east wind), and fall along a parabolic trajectory, and finally fall at a certain position on the ground (X = 94 meters, Y = 190 meters); the prediction result is crucial for subsequent steps (such as the sending of early warning signals in S152 and the determination of early warning aperture in S153), because it helps the detection drone accurately determine the falling position of the invading drone and take corresponding safety measures.

[0123] Furthermore, the falling position of the invading drone relative to the ground is predicted based on the falling trajectory of the invading drone, and the orientation instruction of the lamp body is determined based on the falling position, the posture of the detection drone and the orientation of the lamp body configured on the detection drone. The overall consideration of the falling position, the posture of the detection drone and the orientation of the lamp body configured on the detection drone is compatible to ensure the accuracy of the orientation instruction of the lamp body.

[0124] At this point, in step S151, the drop trajectory of the invading drone has been predicted; next, in step S152, it is necessary to predict the specific drop position of the invading drone relative to the ground based on the drop trajectory; then, combined with the current posture of the detection drone (including position, altitude, flight direction, etc.) and the orientation of the lamp body configured on the detection drone, the directional instructions of the lamp body are determined to illuminate the drop area at the appropriate time and angle and issue a warning signal.

[0125] Based on the drop trajectory determined in step S151, the end point of the trajectory, that is, the expected drop position of the invading drone relative to the ground, is calculated; the expected drop position is usually a two-dimensional coordinate (X, Y), which represents the specific position on the ground; in order to improve the accuracy of the early warning signal, the drop position needs to be predicted as accurately as possible, which requires considering multiple factors, such as slight changes in wind direction, the aerodynamic characteristics of the invading drone, etc.; however, in actual applications, since the above factors are usually difficult to quantify accurately, some simplified models or empirical formulas are used for prediction.

[0126] Optionally, assuming that in step S151, it has been predicted that the invading drone will fall to the ground from a height of 100 meters at a certain speed and angle, and the expected drop position is (X=100 meters, Y=200 meters); based on the calculation of the drop trajectory, it is obtained that the invading drone will fall at the position (X=100 meters, Y=200 meters) on the ground; the expected drop position is a relatively accurate prediction value, although it will be affected by some uncertain factors in actual application.

[0127] The detection drone needs to know its current position and altitude in order to determine the relative distance and angle between it and the predicted drop location. The above information can be obtained through the detection drone's navigation system or GPS; the flight direction and speed of the detection drone will also affect the illumination effect of the lamp and the transmission range of the warning signal; for example, if the detection drone is flying towards the drop location, it will light up the lamp at a closer distance, thereby sending a stronger warning signal.

[0128] The lamp on a detection drone usually has an adjustable mechanical structure to change its illumination direction; the mechanical structure includes rotational joints, pitch joints, etc.; the optimal orientation of the lamp to be adjusted is calculated based on the predicted drop location and the current posture of the detection drone; the optimal orientation should ensure that the light from the lamp can cover the drop location and form a clear warning area on the ground; once the orientation of the lamp is determined, the flight control system of the detection drone will send corresponding instructions to the mechanical structure of the lamp to adjust it to the specified orientation.

[0129] Optionally, based on the predicted drop location and the current posture of the detection drone, the optimal orientation to which the lamp body needs to be adjusted is calculated: a pitch angle of -30 degrees (tilted downward) and an azimuth angle of -60 degrees (pointing towards the drop location); the flight control system of the detection drone sends instructions to the mechanical structure of the lamp body to adjust it to the specified orientation; at this time, the light from the lamp body will cover the drop location and form a clear warning area on the ground, reminding nearby personnel to pay attention to avoid the invading drone that is about to fall.

[0130] Therefore, the warning aperture is determined according to the directional instruction of the lamp body, the luminous range of the lamp body and the luminous mode of the lamp body. The falling position is within the warning aperture, which is compatible with the overall consideration of the directional instruction of the lamp body, the luminous range of the lamp body and the luminous mode of the lamp body to ensure the accuracy of the warning aperture.

[0131] At this time, in step S152, the directional instruction of the lamp body has been determined, that is, the optimal direction to which the lamp body needs to be adjusted to ensure that its light can cover the predicted falling position; next, in step S153, a warning aperture needs to be determined based on the directional instruction of the lamp body, the luminous range of the lamp body, and the luminous mode of the lamp body; the warning aperture should be an area with a certain radius and shape centered on the predicted falling position. When the lamp body is lit, the light intensity in this area should be high enough to form an obvious visual warning.

[0132] Ensure that the lamp body has been adjusted to the optimal orientation according to the directional instructions determined in step S152. This is a prerequisite for ensuring that the warning aperture can accurately cover the drop location. Determine the propagation direction of the light according to the orientation of the lamp body. The propagation direction should be consistent with the predicted drop location to ensure that the light can directly illuminate the location.

[0133] A lamp typically has a specific beam angle, which determines the area the light can cover. This specific beam angle is necessary to calculate the radius of the warning aperture. In addition to the beam angle, the distribution of light intensity also needs to be considered. Typically, light intensity is highest directly in front of the lamp, and decreases as the angle increases. Therefore, the attenuation of light intensity needs to be considered when calculating the warning aperture.

[0134] The lamp body has a variety of lighting modes, such as constant light, flashing, rotating, etc.; different lighting modes will produce different visual effects; it is necessary to select the appropriate lighting mode according to actual needs; the choice of lighting mode should be able to enhance the visual effect of the warning signal and make it more eye-catching; for example, the flashing mode attracts people's attention, and the rotating mode forms a dynamic warning area.

[0135] The shape and size of the warning aperture are determined in combination with the directional instructions, luminous range and luminous mode of the lamp body. Generally, the warning aperture should be a circular or elliptical area with a certain radius centered on the predicted drop location. To ensure the effectiveness of the warning signal, it is necessary to ensure that the light intensity within the warning aperture is high enough, which is achieved by adjusting the brightness, luminous angle or luminous mode of the lamp body.

[0136] In one embodiment of the present application, a warning aperture shape matching table is collected to match the directional instruction, luminous range, and luminous mode of the lamp body with the parameters of the warning aperture; the warning aperture shape matching table is shown in Table 4:

[0137] Table 4 Warning aperture shape matching table

[0138]

[0139]

[0140] Assume that the directional instructions of the lamp body are pitch -30°, azimuth -60°, the lighting range is 60 degrees (horizontal), and the lighting mode is flashing; according to the matching table, the radius of the warning aperture is determined to be 50 meters and the shape is circular; at this time, if the predicted falling position is (X = 100 meters, Y = 200 meters), then this position will be within the warning aperture because the distance between this position and the lamp body is less than the radius of the warning aperture.

[0141] Example 2:

[0142] See also Figure 7 This embodiment provides a drone countermeasure system based on a detection sensor, which implements the drone countermeasure method described in Example 1. Specifically, the drone countermeasure system includes:

[0143] An image acquisition module 21 for determining an aerial image based on visual detection of a detection drone;

[0144] a position determination module 22 for determining an aerial position model of the detection UAV relative to the intrusion UAV based on the aerial image and the distance data detected by the detection sensor;

[0145] The countermeasure module 23 is used to determine the countermeasure signal and countermeasure range based on the air position model, the current working mode of the detection drone, and the flight trajectory of the intruding drone;

[0146] The follow-up module 24 is used to trigger the follow-up countermeasure mode of the detection drone relative to the invading drone according to the escape direction of the invading drone if the invading drone leaves the countermeasure range until the invading drone is in a failure state;

[0147] The warning aperture module 25 is used to detect the falling trajectory of the invading drone based on visual detection, and determine the warning aperture according to the falling trajectory of the invading drone and the direction of the light body of the detection drone.

[0148] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all 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.

Claims

1. A drone countermeasure method based on detection sensors, characterized in that: include: determining aerial imagery based on visual detection by a detection drone; Determine an aerial position model of the detection UAV relative to the intrusion UAV based on the aerial image and the distance data detected by the detection sensor; Determine the countermeasure signal and countermeasure range based on the aerial position model, the current working mode of the detection drone, and the flight trajectory of the intruding drone; If the invading drone leaves the countermeasure range, the detection drone will be triggered to follow the invading drone in a countermeasure mode according to the invading drone's departure direction until the invading drone is in a disabled state; The detection drone captures the falling trajectory of the intruding drone based on visual detection, and determines the warning aperture according to the falling trajectory of the intruding drone and the direction of the detection drone's light body.

2. The drone countermeasure method according to claim 1, characterized in that: Determining an aerial image based on visual detection of the detection drone includes: During the flight of the detection drone, the camera parameters of the multiple cameras are determined according to the flight speed of the detection drone, the environmental parameters of the environment in which the detection drone is located, and the camera parameter mapping relationship; the multiple cameras are all mounted on the detection drone and are mounted at different positions on the detection drone; Based on the imaging parameters of multiple cameras, multiple sub-images centered on the detection drone are obtained. The multiple sub-images correspond to images of the detection drone body in different directions, and the aerial image is obtained by stitching the multiple sub-images.

3. The method for countering drones according to claim 1, characterized in that: Determining an aerial position model of the detection drone relative to the intrusion drone based on the aerial image and the distance data detected by the detection sensor includes: Determine the shape of the intruding drone based on the aerial image, and determine the direction of the detection drone relative to the intruding drone based on the shape of the intruding drone, the position of the intruding drone, and the position of the detection drone, and adjust the flight direction of the detection drone along the direction so that the head of the detection drone faces the intruding drone; The detection sensor is configured in front of the detection drone and is used to obtain distance data between the detection drone and the intrusion drone, and determine the aerial position model of the detection drone relative to the intrusion drone based on the distance data, the position of the detection drone and the position of the intrusion drone.

4. The drone countermeasure method according to claim 1, characterized in that: Determining the countermeasure signal and countermeasure range based on the aerial position model, the current operating mode of the detection drone, and the flight trajectory of the intruding drone includes: In the aerial position model, the relative distance between the detection UAV and the intrusion UAV is updated in real time. If the relative distance is greater than the preset relative distance threshold, the flight speed of the detection UAV is adjusted according to the flight speed and relative distance of the intrusion UAV, so that the relative distance is dynamically maintained within the preset relative distance threshold.

5. The method for countering drones according to claim 1, characterized in that: Determine the countermeasure signal and countermeasure range based on the aerial position model, the current working mode of the detection drone, and the flight trajectory of the intruder drone, including: Collect the current working mode of the detection drone, detect the flight position of the intruding drone based on the camera vision of the detection drone, and determine the flight trajectory of the intruding drone based on the synthesis of multiple flight positions; The first mode coefficient is determined according to the aerial position model and the current working mode of the detection drone, the second mode coefficient is determined according to the aerial position model and the flight trajectory of the invading drone, and the corresponding counter mode is determined according to the mapping relationship between the first mode coefficient, the second mode coefficient and the counter mode. The counter mode covers the corresponding counter signal and counter range.

6. The method for countering drones according to claim 1, characterized in that: If the invading drone leaves the countermeasure range, triggering the follow-up countermeasure mode of the detection drone relative to the invading drone according to the departure direction of the invading drone until the invading drone is in a failure state includes: When the invading drone is countered by the detection drone, the flight speed of the invading drone is further increased to escape from the counterattack range. At this time, the escape direction of the invading drone is collected based on the visual detection of the detection drone.

7. The method for countering drones according to claim 1, characterized in that: If the invading drone leaves the countermeasure range, triggering the detection drone to follow the invading drone in a countermeasure mode according to the invading drone's departure direction until the invading drone is in a malfunction state, further comprising: Determine a direction adjustment command for the detection drone based on the intruder drone's departure position and the detection drone's flight direction, and trigger the detection drone's flight turn based on the direction adjustment command, so that the detection drone continues to face the intruder drone, and triggers the detection drone's follow-up countermeasure mode relative to the intruder drone; In this follow-up countermeasure mode, the detection drone follows the intrusion drone and further expands the countermeasure range according to the parameter adjustment of the detection drone until the intrusion drone is successfully countered by the detection drone and the intrusion drone is in a failure state.

8. The method for countering drones according to claim 1, characterized in that: The detection drone captures the falling trajectory of the intruding drone based on visual detection, and determines the warning aperture according to the falling trajectory of the intruding drone and the direction of the light body of the detection drone, including: After the invading drone is in a malfunctioning state, it descends and is unable to fly. The detection drone captures multiple landing locations of the invading drone based on visual detection, and determines the falling trajectory of the invading drone based on the multiple landing locations, the wind direction of the current environment, and the weight of the invading drone.

9. The method for countering drones according to claim 8, characterized in that: The detection drone captures the falling trajectory of the intruding drone based on visual detection, and determines the warning aperture according to the falling trajectory of the intruding drone and the direction of the light body of the detection drone, further comprising: Predicting the drop position of the intruding drone relative to the ground based on the drop trajectory of the intruding drone, and determining the orientation instruction of the lamp body based on the drop position, the posture of the detection drone, and the orientation of the lamp body configured for the detection drone; The warning aperture is determined according to the directional instruction of the lamp body, the lighting range of the lamp body and the lighting mode of the lamp body, and the falling position is within the warning aperture.

10. A drone countermeasure system based on detection sensors, characterized in that: The detection sensor-based drone countermeasure system is applied to the detection sensor-based drone countermeasure method according to any one of claims 1 to 9, and the detection sensor-based drone countermeasure system includes: an aerial image module for determining aerial imagery based on visual detection by a detection drone; An aerial position module is used to determine an aerial position model of the detection UAV relative to the intrusion UAV based on the aerial image and the distance data detected by the detection sensor; The countermeasure module is used to determine the countermeasure signal and countermeasure range based on the aerial position model, the current working mode of the detection drone, and the flight trajectory of the intruding drone; The follow-up module is used to trigger the follow-up countermeasure mode of the detection drone relative to the invading drone according to the escape direction of the invading drone if the invading drone leaves the countermeasure range until the invading drone is in a failure state; The early warning aperture module is used to detect drones and capture the falling trajectory of intruding drones based on visual detection. The early warning aperture is determined according to the falling trajectory of the intruding drone and the direction of the light body of the detection drone.

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