Drone countermeasure method and system based on detection sensors

Abstract

The application discloses a kind of unmanned plane countermeasure method and system based on detection sensor, according to aerial position model, the current working mode of detection unmanned plane and the flight trajectory of invading unmanned plane determine countermeasure signal and countermeasure range, realize detection unmanned plane to the visual detection of invading unmanned plane, and ensure that detection unmanned plane to the countermeasure effect of invading unmanned plane.If invading unmanned plane leaves countermeasure range, then according to the direction of invading unmanned plane triggers detection unmanned plane relative to the follow-up countermeasure mode of invading unmanned plane;Detection unmanned plane is based on visual detection to capture the falling trajectory of invading unmanned plane, according to the falling trajectory of invading unmanned plane and the orientation of the lamp body of detection unmanned plane determines warning ring, realizes the follow-up countermeasure of detection unmanned plane relative to invading unmanned plane, and detection unmanned plane is based on visual detection to control the falling position of invading unmanned plane, and dynamic warning is carried out through warning ring.

Description

Technical Field

[0001] This invention relates to the technical field of drone countermeasures, and more particularly to a drone countermeasures method and system based on detection sensors. Background Technology

[0002] With the development of technology, drones are used as components of aerial patrol. At this time, detection drones are generally used as patrol prototypes and patrol in the air. Intrusion drones are drones to be controlled and are subject to countermeasures by detection drones. In the existing technology, detection drones countermeasure in the air based on the real-time location of intrusion drones, without taking into account the flight trajectory of intrusion drones, thus reducing the effectiveness of detection drones in countermeasures against intrusion drones. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and system for countering unmanned aerial vehicles (UAVs) based on detection sensors.

[0004] This invention provides a method for countering unmanned aerial vehicles (UAVs) based on detection sensors, comprising:

[0005] Aerial images are determined based on visual detection by the reconnaissance drone;

[0006] The aerial position model of the detection drone relative to the intrusion drone is determined based on aerial imagery and distance data detected by detection sensors.

[0007] The countermeasure signal and countermeasure range are determined based on the aerial position model, the current operating mode of the detection drone, and the flight trajectory of the intruding drone.

[0008] If the intrusion drone leaves the countermeasure range, the detection drone will be triggered to follow the countermeasure mode relative to the intrusion drone based on the direction of departure of the intrusion drone, until the intrusion drone is in a malfunctioning state.

[0009] The detection drone uses visual detection to capture the fall trajectory of the intruding drone, and determines the warning light circle based on the fall trajectory of the intruding drone and the orientation of the detection drone's lights.

[0010] This invention provides a drone countermeasure system based on a detection sensor. The drone countermeasure system based on a detection sensor is applied to the aforementioned drone countermeasure method based on a detection sensor. The drone countermeasure system based on a detection sensor includes:

[0011] An aerial image module is used to determine aerial images based on visual detection of the reconnaissance drone;

[0012] An aerial position module is used to determine the aerial position model of the detection drone relative to the intruding drone based on aerial imagery and distance data detected by detection sensors.

[0013] The countermeasure module is used to determine 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;

[0014] The follow-up module is used to trigger the follow-up countermeasure mode of the detection drone relative to the intrusion drone based on the direction of the intrusion drone's departure if the intrusion drone leaves the countermeasure range, until the intrusion drone is in a malfunctioning state.

[0015] The warning aperture module is used to detect drones by visually capturing the fall trajectory of intruding drones and to determine the warning aperture based on the fall trajectory of the intruding drone and the orientation of the detection drone's lights.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] In this embodiment of the invention, the method described herein determines an aerial image based on visual detection by the detection drone; determines an aerial position model of the detection drone relative to the intruding drone based on the aerial image and distance data detected by the detection sensor; and determines a 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. This method incorporates overall considerations of the countermeasure signal and countermeasure range, and covers the flight trajectory of the intruding drone, thereby achieving visual detection of the intruding drone by the detection drone and ensuring the countermeasure effect of the detection drone against the intruding drone.

[0018] Therefore, if the intrusion drone leaves the countermeasure range, the detection drone will be triggered to follow the intrusion drone's countermeasure mode based on the intrusion drone's departure direction until the intrusion drone is in a malfunctioning state. The detection drone captures the intrusion drone's fall trajectory based on visual detection, and determines the warning light circle based on the intrusion drone's fall trajectory and the orientation of the detection drone's lights, thus realizing the follow-up countermeasure of the detection drone relative to the intrusion drone and ensuring the dynamic countermeasure effect of the detection drone. At the same time, the detection drone controls the fall position of the intrusion drone based on visual detection and provides dynamic warnings through the warning light circle. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the UAV countermeasure method based on detection sensors in this invention.

[0020] Figure 2 This is a flowchart illustrating step S11 of the UAV countermeasure method based on detection sensors in this invention.

[0021] Figure 3 This is a flowchart illustrating step S12 in the UAV countermeasure method based on detection sensors of the present invention.

[0022] Figure 4 This is a flowchart illustrating step S13 in the UAV countermeasure method based on detection sensors of the present invention.

[0023] Figure 5 This is a flowchart illustrating step S14 of the UAV countermeasure method based on detection sensors in this invention.

[0024] Figure 6 This is a flowchart illustrating step S15 of the UAV countermeasure method based on detection sensors in this invention.

[0025] Figure 7 This is a schematic diagram of the structural composition of the drone countermeasure system based on detection sensors in this invention. Detailed Implementation

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

[0027] Example 1:

[0028] Please see Figures 1 to 7 This embodiment provides a drone countermeasure method based on detection sensors, applied to drone countermeasure scenarios based on detection sensors. The drone countermeasure method includes:

[0029] Step S11: Acquire aerial images using the visual imaging system of the detection drone;

[0030] Step S12: Determine the 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 sensors;

[0031] Step S13: 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;

[0032] Step S14: If the intrusion drone leaves the countermeasure range, the detection drone will be triggered to follow the countermeasure mode relative to the intrusion drone based on the direction of departure of the intrusion drone, until the intrusion drone is in a malfunctioning state.

[0033] Step S15: The detection drone uses a visual detection system to capture the fall trajectory of the intrusion drone and determines the warning aperture based on the fall trajectory of the intrusion drone and the orientation of the lighting device on 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, the camera parameters of multiple cameras are determined based on the flight speed of the detection drone, the environmental parameters of the environment in which the detection drone is located, and the mapping relationship of camera parameters; multiple cameras are mounted on the detection drone and mounted at different positions on the detection drone, such as in front, behind, above, below, left, and right of the detection drone body, to provide an all-round imaging field of view;

[0036] S112: Based on the camera parameters of multiple cameras, acquire multiple sub-images centered on the detection drone. The multiple sub-images correspond to images of different directions of the detection drone body (such as one or more of the front, rear, top, bottom, left, and right sides of the detection drone body), and obtain an aerial image by stitching together the multiple sub-images.

[0037] During the flight of a detection drone, the drone's flight speed, the environmental parameters of the drone's environment, and the mapping relationship of camera parameters all 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 blurring; at the same time, the frame rate also needs to be increased to ensure that continuous clear images are captured; at the same time, the UAV flight control system will monitor the flight speed in real time and automatically adjust the camera parameters according to the preset mapping relationship, wherein the camera's imaging parameters include the camera's exposure time.

[0039] Suppose a reconnaissance drone is equipped with four cameras positioned in front, behind, left, and right of the drone, and is passing through a region with thick clouds, resulting in low light levels when the cameras capture images. In this situation, the drone's flight control system monitors its flight speed in real time and automatically adjusts the angle of the camera gimbal and the camera parameters accordingly. For example, if the flight speed is low, the drone will take a longer time to pass through the cloud area. In this case, the flight control system automatically adjusts the gimbal angle, which is within the range of 0-10°, shortens the camera's exposure time to 1 / 1000 of a second, increases the frame rate to 60 frames per second, and increases the camera gain, while adjusting contrast and sharpness to ensure that a clear image is captured.

[0040] Furthermore, the camera parameter mapping relationship refers to the correspondence between camera configuration (such as position, lens orientation angle, focal length) and camera parameters (such as exposure time, frame rate, gain, contrast, etc.). This mapping relationship can be determined experimentally during the design and testing phase of the UAV 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 based on the current camera configuration (such as the rotation angle of the mechanical gimbal and the camera focal length).

[0041] For example, if a camera is adjusted to an overhead angle, the flight control system will select appropriate parameters such as focal length and exposure time for the overhead view. The images acquired by the aforementioned cameras are processed and synthesized in real time by the UAV's flight control system to form a comprehensive aerial image, providing crucial information for subsequent countermeasures. Thus, the detection UAV can automatically adjust the camera parameters in complex aerial environments to capture high-quality images, providing strong support for subsequent countermeasures missions.

[0042] At this time, during the flight of the detection drone, its flight control system will trigger the visual detection function based on the previously determined camera parameters (such as exposure time, frame rate, gain, etc.). This means that the camera will start capturing images, and the images will be optimized based on 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, suppose the detection drone is conducting an aerial patrol mission with its four cameras located in front, behind, to the left, and to the right. At this time, the drone is flying over an urban area with many tall buildings and roads around it. The flight control system triggers the visual detection function of the four cameras based on the previously determined camera parameters (such as an exposure time of 1 / 500 second and a frame rate of 30 frames per second).

[0044] The flight control system receives sub-images from various cameras and uses image stitching or fusion algorithms to combine the sub-images into a complete aerial image. The aerial image provides a comprehensive view of the environment around the drone. Meanwhile, the image stitching algorithm takes into account factors such as overlapping areas between cameras, differences in viewing angles, and image distortion to ensure that the synthesized image is accurate and seamless. The fusion algorithm focuses more on improving the contrast and clarity of the image, as well as reducing noise and artifacts.

[0045] Optionally, the flight control system receives four sub-images and uses an image stitching algorithm to combine them 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 buildings and streets on the left and right. Through the synthesized image, obstacles, roads, and potential targets around the drone are clearly visible. Thus, the detection drone can combine sub-images captured by multiple cameras into a comprehensive aerial image, providing key information for subsequent countermeasures, which greatly improves the drone's perception capabilities and safety in complex environments.

[0046] In one embodiment of this 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, as 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 begin to capture images, and then the flight control system uses an image stitching algorithm to combine the sub-images into a complete aerial image.

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

[0051] S121: Determine the shape of the intrusion drone based on the aerial image, and determine the direction of the detection drone relative to the intrusion drone based on the shape of the intrusion drone, the position of the intrusion 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 intrusion drone.

[0052] S122: The detection sensor is positioned in front of the detection drone to acquire distance data between the detection drone and the intrusion drone, and to 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.

[0053] The shape of the intruding drone can be determined by the visual detection system of the drone. For example, the visual detection system of the drone can use image recognition algorithms (such as convolutional neural networks CNN) to identify the intruding drone in the aerial image and obtain the shape parameters of the intruding drone based on the recognition results. This process includes steps such as feature extraction, target classification and bounding box localization. The shape parameters of the intruding drone include one or more of the following: the size of the drone (which can be estimated based on the size of the target detection box), shape (e.g., quadcopter drone), and model.

[0054] After identifying the intruding drone, the visual detection system calculates the intruding drone's position coordinates 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 identification experience), combined with parameters such as the drone's flight altitude and the camera's angle of view. The position coordinates of the intruding drone are relative to the current position of the detection drone.

[0055] With the position coordinates of the intrusion drone and the position coordinates of the detection drone (which can be obtained via GPS or other navigation devices), the flight control system calculates the relative position between the two, which is represented by 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 (as well as altitude) in order to fly toward the intruding drone. This process requires real-time updates and adjustments because the position and speed of the intruding drone may change. The flight control system uses an autopilot or flight algorithms to ensure that the drone can fly smoothly and accurately along the predetermined direction.

[0057] Based on the pixel coordinates in the image and the actual size of the intruding drone, combined with parameters such as the drone's flight altitude and the camera's angle of view, the system calculates that the intruding drone is located approximately 300 meters northeast of the detection drone. The flight control system, based on the position coordinates of the two drones, calculates that the detection drone needs to turn approximately 45 degrees northeast to face the intruding drone. Upon receiving the command, the flight control system begins adjusting the detection drone's heading and speed, gradually turning it northeast and approaching the intruding drone. During flight, the flight control system continuously monitors and adjusts flight parameters to ensure the drone flies smoothly and accurately along the predetermined direction. Thus, the detection drone can flexibly adjust its flight direction to cope with changes in the position and shape of the intruding drone, thereby achieving effective surveillance and tracking.

[0058] Furthermore, at this point, one or more types of detection sensors are typically configured in front of the detection drone, such as LiDAR, infrared sensors, radar systems, etc.; the specific type of these sensors depends 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, it is assumed that when the detection drone is performing an aerial surveillance mission, it detects the presence of the intruding drone through a lidar sensor in front of it; the lidar sensor is configured in front of the detection drone, and it actively emits laser pulses and receives the reflected signals.

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

[0061] In addition to distance data, it is also necessary to know the specific location information of the detection drone and the intrusion drone, which is obtained through GPS system, inertial navigation system (INS) or other positioning technology; at the same time, using the principles of geometry and spatial analytic geometry, combined with distance data and location information, an 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 utilizing information and geometric principles, an aerial position model of the detection drone relative to the intrusion drone can be calculated. For example, it can be calculated that the detection drone needs to fly at an angle of about 45 degrees east of north to approach the intrusion drone and maintain a relative distance of about 300 meters (the relative distance will be dynamically adjusted considering the movement and errors of the drone).

[0063] As time goes on, 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 performance of the aerial position model. As a result, the detection drone can accurately determine its relative position to the intrusion drone, providing strong support for subsequent mission execution (such as tracking, surveillance, and countermeasures).

[0064] refer to Figure 4 In step S13, the countermeasure signal and countermeasure range are determined based on the aerial position model, the current operating 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 drone and the intrusion drone is updated in real time. If the relative distance is greater than the preset relative distance threshold, the flight speed of the detection drone is adjusted according to the flight speed of the intrusion drone and the relative distance, so that the relative distance is dynamically maintained within the preset relative distance threshold.

[0066] S132: Collect the current working mode of the detection drone, detect the flight position of the intrusion drone based on the camera vision of the detection drone, and determine the flight trajectory of the intrusion drone based on the synthesis of multiple flight positions.

[0067] S133: Determine the first mode coefficient based on the air position model and the current working mode of the detection drone; determine the second mode coefficient based on the air position model and the flight trajectory of the intruding drone; determine the corresponding countermeasure mode based on the first mode coefficient, the second mode coefficient and the countermeasure mode mapping relationship, which includes the corresponding countermeasure signal and countermeasure range.

[0068] In the embodiments of this application, the relative distance between the detection drone and the intrusion drone is updated in real time in the aerial position model. 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 of the intrusion drone and the relative distance, so that the relative distance is dynamically maintained within the preset relative distance threshold, thus ensuring that the detection drone tracks the intrusion drone.

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

[0070] Based on mission requirements and security considerations, a relative distance threshold is set; the threshold represents the maximum allowable distance between the detection drone and the intrusion drone; 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 drone needs to adjust its flight speed to approach the intrusion drone.

[0071] Based on the flight speed and relative distance of the intrusion drone, the flight speed that the detection drone needs to adjust is calculated. This usually involves the synthesis and decomposition of velocity vectors, as well as the calculation of acceleration and deceleration. The calculated flight speed is then sent to the flight control system of the detection drone to adjust the drone's speed. Since the flight speed and direction of the intrusion 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 intrusion drone is detected based on the camera vision of the detection drone, and the flight trajectory of the intrusion drone is determined by synthesizing multiple flight positions, thus ensuring the accuracy of the flight trajectory of the intrusion drone.

[0073] At this time, the detection drone has multiple working modes, such as tracking, surveillance, patrolling, and countermeasures; each working mode corresponds to different mission requirements and flight strategies; at the same time, the information of 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 data collection process involves reading the stored data, parsing the communication protocol, 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] The detection drone is usually equipped with a high-definition camera to capture images of the surrounding environment in real time. The images captured by the camera are processed by computer vision algorithms to identify and locate the flight position of the intrusion drone, which involves steps such as target detection, tracking, and feature extraction. The visual detection algorithm outputs the real-time position data of the intrusion drone, including two-dimensional image coordinates or three-dimensional spatial coordinates.

[0075] The continuously captured flight position data of the intrusion 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 in missing data, and estimate the actual flight trajectory of the intrusion drones; finally, the flight trajectory of the intrusion drones is output, which can be displayed in two-dimensional or three-dimensional graphical form, or provided to the subsequent processing modules in numerical data form.

[0076] Therefore, the first mode coefficient is determined based on the aerial position model and the current working mode of the detection drone, the second mode coefficient is determined based on the aerial position model and the flight trajectory of the intruding drone, and the corresponding countermeasure mode is determined based on the first mode coefficient, the second mode coefficient and the countermeasure mode mapping relationship. This countermeasure mode covers the corresponding countermeasure signal and countermeasure range, and is compatible with the overall consideration of the first mode coefficient, the second mode coefficient and the countermeasure mode mapping relationship, so as to ensure the accuracy of the corresponding countermeasure mode.

[0077] At this time, the aerial position model contains information such as the relative position, speed, and acceleration 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 countermeasure; 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 detection drone is currently in "interception mode" and, according to the aerial position model, it is rapidly approaching the intruding drone with good speed matching and obvious positional 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] Regarding the second mode coefficient, the flight trajectory of the intruding 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, and flight altitude; based on the trajectory features and the aerial position model, the second mode coefficient is calculated using a preset algorithm. The second mode coefficient reflects the dynamic behavior or potential threat of the intruding drone.

[0080] For example, by analyzing the flight trajectory of the intrusion drone, it was found that its speed gradually decreased, its direction deviated, and its flight altitude decreased. These characteristics indicate that the intrusion drone is performing evasive maneuvers or has encountered a malfunction. The second mode coefficient was calculated to be 0.6 (indicating that the threat of the intrusion drone has decreased, but vigilance is still necessary).

[0081] The countermeasure mode mapping relationship is a preset mapping table or algorithm that 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 the countermeasure signal (such as the jamming signal, the interception signal, etc.) and the countermeasure range (such as the jamming radius, the interception area, etc.). The above parameters determine how to effectively counter the intruding drone.

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

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

[0084] Table 2 Countermeasure Pattern Matching Table

[0085]

[0086] Assuming the first mode coefficient is "medium" (indicating that the detection drone is in a medium-efficiency state) and the second mode coefficient is "high" (indicating that the intruding 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 directional area".

[0087] refer to Figure 5 In step S14, if the intrusion drone leaves the countermeasure range, the detection drone is triggered to follow the countermeasure mode relative to the intrusion drone based on the direction of departure of the intrusion drone until the intrusion drone is in a malfunctioning state.

[0088] In the specific implementation of this invention, the specific steps are as follows:

[0089] S141: When the intrusion drone is countered by the detection drone, the intrusion drone further increases its flight speed to escape the countermeasure range. At this time, the escape location of the intrusion drone is collected based on the visual detection of the detection drone.

[0090] S142: Determine the direction adjustment command for the detection drone based on the departure position of the intrusion drone and the flight direction of the detection drone, and trigger the flight turn of the detection drone according to the direction adjustment command, so that the detection drone continues to face the intrusion drone, and trigger the follow-up countermeasure mode of the detection drone relative to the intrusion drone.

[0091] S143: In this follow-up countermeasure mode, the detection drone follows the intrusion drone and further expands the countermeasure range according to the parameters of the detection drone until the intrusion drone is successfully countered by the detection drone and the intrusion drone is in a malfunctioning state.

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

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

[0094] In response to countermeasures from reconnaissance drones, intrusion drones will take countermeasures, one of which is to increase their flight speed in an attempt to escape the reconnaissance drone's countermeasure range. The speed increase of the intrusion drone is sudden and gradual, depending on its flight control system and current power status.

[0095] The system detects and analyzes the surrounding environment in real time. When an intruding drone begins to accelerate and attempts to escape the countermeasures range, the detection drone's visual detection system can capture this change and calculate the intruding drone's departure position relative to the detection drone. The departure position typically 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 continuously updates the intruding drone's position and departure position to ensure that the detection drone can track its dynamics in real time.

[0096] Furthermore, based on the departure position of the intrusion drone and the flight direction of the detection drone, the direction adjustment command of the detection drone is determined, and the flight turn of the detection drone is triggered according to the direction adjustment command, so that the detection drone continues to face the intrusion drone, and the follow-up countermeasure mode of the detection drone relative to the intrusion drone is triggered. This takes into account both the departure position of the intrusion drone and the flight direction of the detection drone as a whole, ensuring the accuracy of the direction adjustment command of the detection drone.

[0097] At this point, the input information includes the departure position of the intruding 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 command through a preset algorithm or logic. The direction adjustment command is intended to enable the detection drone to adjust its flight direction so as to continue to face the intruding drone. The direction adjustment command usually includes information such as the adjustment angle, rate, and the required flight altitude or speed adjustment.

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

[0099] The flight control system of a reconnaissance drone is responsible for executing directional adjustment commands, which typically involve actuators such as the drone's control surfaces, engine thrust distribution, or vector thrust system. Based on the directional adjustment commands, the flight control system will trigger corresponding steering operations. 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 operations.

[0100] Once the detection drone successfully adjusts its flight direction and continues to face the intruding 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 intruding 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 a follow-up countermeasure mode. In follow-up countermeasure mode, the detection drone begins to emit jamming signals in an attempt to interfere with the communication and navigation systems of the intruding drone. At the same time, the system continuously analyzes the flight trajectory and speed changes of the intruding drone in order to adjust the strength and frequency of the jamming signals as needed. As a result, 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 intrusion drone and further expands the countermeasure range according to the parameters of the detection drone until the intrusion drone is successfully countered by the detection drone and the intrusion drone is in a malfunctioning state.

[0103] At this point, homing flight refers to the detection drone dynamically adjusting its flight trajectory and speed based on the real-time position and dynamics of the intrusion drone in order to maintain close tracking of the intrusion drone. It involves the flight control system of the detection drone, which continuously receives data from various sensors on the drone (such as GPS position, speed, altitude, attitude, etc.) and combines it with the position and dynamic information of the intrusion drone (such as information obtained through visual detection or radar systems) to calculate and adjust the drone's flight parameters. The goal of homing flight is to ensure that the detection drone can always remain within the effective countermeasure range of the intrusion drone, no matter how the intrusion drone changes its flight trajectory or speed.

[0104] Optionally, suppose that when the detection drone is performing an aerial interception mission, it successfully locks onto an unauthorized intrusion drone; 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 the tracking, the detection drone's flight control system continuously adjusts its flight parameters to maintain close tracking; for example, when the intrusion drone suddenly climbs, the detection drone also rapidly increases thrust and adjusts its attitude to follow its ascent trajectory.

[0105] During homing flight, the flight control system of the detection drone adjusts various flight parameters of the drone as needed, such as speed, altitude, and attitude, in order to optimize the drone's tracking performance and countermeasures. As the detection drone tracks the intruding drone more closely, the system gradually increases the strength or range of countermeasures according to preset strategies or algorithms. For example, if the intruding drone attempts to evade countermeasures through high-speed maneuvers, the detection drone increases the power or range of the interference signal to ensure continuous and effective countermeasures. Parameter adjustment and countermeasure range expansion is a dynamic process that needs to be continuously optimized and adjusted based on the real-time response of the intruding drone and the current state of the detection drone.

[0106] The standard for successful countermeasures is usually that the intruding drone is unable to continue its original mission or maintain a stable flight state. This is manifested by interference with the intruding drone's communication system, failure of its navigation system, or failure of its power system. Once the intruding drone reaches a malfunctioning state, it will lose control, crash, or be forced to land, depending on the type and strength of the countermeasures taken against the detection drone, as well as the design and performance of the intruding drone. After a successful countermeasure, the detection drone will perform a series of follow-up 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 intruding drone is severely interfered with, and its navigation system also begins to fail. Ultimately, the intruding drone loses control, begins to fly erratically, and eventually crashes in an open area. After confirming the success of the countermeasures, the detection drone records relevant data and reports the results to the command center. Thus, the detection drone can effectively track and counter intruding drones in complex and ever-changing aerial environments, thereby ensuring airspace security and successful mission execution.

[0108] In one embodiment of this application, a phase matching table is used to illustrate how the detection drone adjusts its own parameters and countermeasures based on the state of the intruding drone at different stages; the phase matching table is shown in Table 3:

[0109] Table 3 Matching Table

[0110]

[0111] refer to Figure 6 In step S15, the detection drone captures the fall trajectory of the intrusion drone based on visual detection, and determines the warning aperture based on the fall trajectory of the intrusion drone and the orientation of the detection drone's light body.

[0112] In the specific implementation of this invention, the specific steps are as follows:

[0113] S151: After the intrusion drone malfunctions, it descends and is unable to fly. The detection drone captures multiple landing positions of the intrusion drone based on visual detection and determines the fall trajectory of the intrusion drone based on the multiple landing positions, the current wind direction, and the weight of the intrusion drone.

[0114] S152: Predict the landing position of the intrusion drone relative to the ground based on the landing trajectory of the intrusion drone, and determine the orientation command of the light body based on the landing position, the attitude of the detection drone, and the orientation of the light body configured on the detection drone.

[0115] S153: The warning light circle is determined based on the directional command of the lamp body, the light emission range of the lamp body, and the light emission mode of the lamp body. The falling position is within the warning light circle.

[0116] In the embodiments of this application, after the intrusion drone malfunctions, it descends and is unable to fly. The detection drone captures multiple landing positions of the intrusion drone based on visual detection, and determines the falling trajectory of the intrusion drone based on the multiple landing positions, the current wind direction, and the weight of the intrusion drone. This overall consideration of multiple landing positions, the current wind direction, and the weight of the intrusion drone ensures the accuracy of the falling trajectory of the intrusion drone.

[0117] At this point, after the intrusion drone is rendered inoperable due to the countermeasures of the detection drone, it will begin to descend. At this time, the detection drone needs to use its visual detection system to capture multiple landing positions of the intrusion drone and comprehensively consider multiple factors (including multiple landing positions, the current wind direction, and the weight of the intrusion drone) to determine the final falling trajectory of the intrusion drone.

[0118] Meanwhile, 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 positions at different times, and the above position data can be used as the basis for subsequent analysis.

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

[0120] The reconnaissance drone is equipped with a weather sensor to detect the wind direction and speed of the current environment in real time; if there is no weather sensor, the system also obtains the above information from other sources (such as weather station data); wind direction has a significant impact on the landing trajectory of the intrusion drone; for example, if the wind is from the north, the intrusion drone will fall slightly to the south; the system needs to take into account both wind direction and wind speed to calculate the offset.

[0121] The detection drone knows the model and weight of the intruding drone in advance, or identifies its model through a visual inspection system and queries the corresponding weight data from a database. The weight of the intruding drone affects its susceptibility to wind and gravity; heavier drones are less affected by wind during descent, while lighter drones are more easily blown by the wind. The system performs comprehensive calculations based on these factors (multiple landing locations, wind direction, weight) to determine the final fall trajectory of the intruding drone. The final fall trajectory prediction should be a dynamic process, as the position and speed of the intruding drone change continuously during descent; the detection drone needs to continuously update its prediction results.

[0122] Optionally, the detection drone identifies the intrusion drone model as XX-123 using a visual detection system and retrieves the weight of this model from the database as 5 kg. After analysis, the system concludes that the weight of the intrusion drone will be slightly affected by wind during its descent. Considering these factors, the detection drone predicts the final descent trajectory of the intrusion drone. For example, the system predicts that the intrusion drone will start from its initial position, slightly deviate westward (influenced by easterly winds), and fall along a parabolic trajectory, eventually landing at a certain location on the ground (X = 94 meters, Y = 190 meters). The prediction result is crucial for subsequent steps (such as sending the warning signal in S152 and determining the warning aperture in S153) because it helps the detection drone accurately determine the landing location of the intrusion drone and take appropriate safety measures.

[0123] Furthermore, the landing position of the intrusion drone relative to the ground is predicted based on the landing trajectory of the intrusion drone, and the orientation command of the light body is determined based on the landing position, the attitude of the detection drone, and the orientation of the light body configured on the detection drone. This comprehensive consideration of the landing position, the attitude of the detection drone, and the orientation of the light body configured on the detection drone ensures the accuracy of the orientation command of the light body.

[0124] At this point, in step S151, the trajectory of the intrusion drone's fall has been predicted. Next, in step S152, the specific fall location of the intrusion drone relative to the ground needs to be predicted based on the fall trajectory. Then, by combining the current attitude of the detection drone (including position, altitude, flight direction, etc.) and the orientation of the lights configured on the detection drone, the directional command of the lights is determined so that the fall area can be illuminated at the appropriate time and angle to issue a warning signal.

[0125] Based on the drop trajectory determined in step S151, the endpoint of the trajectory is calculated, which is the expected drop position of the intruding drone relative to the ground. The expected drop position is usually a two-dimensional coordinate (X,Y), representing the specific location on the ground. To improve the accuracy of the warning signal, the drop position needs to be predicted as accurately as possible, which requires considering various factors, such as slight changes in wind direction and the aerodynamic characteristics of the intruding drone. However, in practical applications, since the above factors are usually difficult to quantify precisely, some simplified models or empirical formulas are used for prediction.

[0126] Optionally, it is assumed that in step S151, the intrusion drone has been predicted to fall to the ground from a height of 100 meters at a certain speed and angle, with the expected landing location being (X = 100 meters, Y = 200 meters); based on the calculation of the falling trajectory, the location on the ground where the intrusion drone will fall is obtained (X = 100 meters, Y = 200 meters); the expected landing location is a relatively accurate prediction, although it may be affected by some uncertain factors in actual applications.

[0127] The detection drone needs to know its current position and altitude in order to determine the relative distance and angle between itself and the predicted crash location. This information can be obtained through the detection drone's navigation system or GPS. The drone's flight direction and speed also affect the illumination effect of the light and the range of the warning signal. For example, if the detection drone is flying towards the crash location, it will illuminate the light at a closer distance, thus emitting a stronger warning signal.

[0128] The lights on a detection drone typically have adjustable mechanical structures to change their illumination direction. These mechanical structures include rotational joints, pitch joints, etc. By combining the predicted drop location with the current attitude of the detection drone, the optimal orientation that the lights need to be adjusted to is calculated. The optimal orientation should ensure that the light beams can cover the drop location and create a clear warning zone on the ground. Once the orientation of the lights is determined, the flight control system of the detection drone sends corresponding instructions to the mechanical structures of the lights to adjust them to the specified orientation.

[0129] Optionally, combining the predicted drop location and the current attitude of the detection drone, the optimal orientation that the light body needs to be adjusted to is calculated as follows: pitch angle of -30 degrees (tilting downwards) and azimuth angle of -60 degrees (pointing to the drop location); the flight control system of the detection drone sends instructions to the mechanical structure of the light body to adjust it to the specified orientation; at this time, the light body's light will cover the drop location and form a clear warning zone on the ground, reminding nearby personnel to pay attention to avoid the intruding drone that is about to fall.

[0130] Therefore, the warning light circle is determined based on the directional command, the light emission range, and the light emission mode of the lamp body. The falling position is within the warning light circle. The overall consideration of the directional command, the light emission range, and the light emission mode of the lamp body ensures the accuracy of the warning light circle.

[0131] At this point, in step S152, the orientation command of the lamp body has been determined, that is, the optimal orientation that the lamp body needs to be adjusted to ensure that its light can cover the predicted drop location. Next, in step S153, a warning light circle needs to be determined based on the orientation command of the lamp body, the light emission range of the lamp body, and the light emission mode of the lamp body. The warning light circle should be an area with a certain radius and shape centered on the predicted drop location. When the lamp body is lit, the light intensity in this area should be high enough to form a clear visual warning.

[0132] Ensure that the lamp body has been adjusted to the optimal orientation according to the orientation instructions determined in step S152, which is a prerequisite for ensuring that the warning light circle can accurately cover the drop location; determine the direction of light propagation based on the orientation of the lamp body; the direction of propagation should be consistent with the predicted drop location to ensure that the light can directly illuminate that location.

[0133] The lamp body usually has a certain beam angle, which determines the size of the area that the light can cover. The specific beam angle of the lamp body needs to be known in order to calculate the radius of the warning light circle. In addition to the beam angle, the distribution of light intensity also needs to be considered. Usually, the light intensity is the highest directly in front of the lamp body. As the angle increases, the intensity will gradually decrease. Therefore, when calculating the warning light circle, the attenuation of light intensity needs to be considered.

[0134] The lamp body has multiple light emission modes, such as constant light, flashing, and rotating. Different light emission modes produce different visual effects. The appropriate light emission mode needs to be selected according to actual needs. The selection of the light emission mode should enhance the visual effect of the warning signal and make it more eye-catching. For example, the flashing mode attracts people's attention, while the rotating mode forms a dynamic warning zone.

[0135] The shape and size of the warning light circle are determined by combining the directional instructions, light emission range, and light emission mode of the light body. Typically, the warning light circle 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 light circle is high enough, which is achieved by adjusting the brightness, light emission angle, or light emission mode of the light body.

[0136] In one embodiment of this application, a warning aperture shape matching table is collected, and the directional command, light emission range, and light emission mode of the lamp body are matched 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] Assuming the light's orientation command is -30° pitch, -60° azimuth, a 60-degree (horizontal) illumination range, and a flashing illumination mode; based on the matching table, the radius of the warning light circle is determined to be 50 meters, and its shape is circular; at this time, if the predicted drop location is (X = 100 meters, Y = 200 meters), then this location will be within the warning light circle because the distance between this location and the light is less than the radius of the warning light circle.

[0141] Example 2:

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

[0143] Image acquisition module 21 is used to determine aerial images based on visual detection of the detection drone;

[0144] The position determination module 22 is used to determine the aerial position model of the detection drone relative to the intrusion drone based on aerial images and distance data detected by the detection sensors.

[0145] Countermeasure module 23 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.

[0146] The follow-up module 24 is used to trigger the follow-up countermeasure mode of the detection drone relative to the intrusion drone based on the direction of the intrusion drone's departure if the intrusion drone leaves the countermeasure range, until the intrusion drone is in a malfunctioning state.

[0147] The warning aperture module 25 is used to detect the fall trajectory of the intruding drone based on visual detection, and to determine the warning aperture based on the fall trajectory of the intruding drone and the orientation of the light body of the detection drone.

[0148] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, 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 method for countering unmanned aerial vehicles (UAVs) based on detection sensors, characterized in that, include: Aerial images are determined based on visual detection by the reconnaissance drone; The aerial position model of the detection drone relative to the intrusion drone is determined based on aerial imagery and distance data detected by detection sensors. The countermeasure signal and countermeasure range are determined based on the aerial position model, the current operating mode of the detection drone, and the flight trajectory of the intrusion drone. This includes collecting the current operating mode of the detection drone, detecting the flight position of the intrusion drone based on the camera vision of the detection drone, and determining the flight trajectory of the intrusion drone by synthesizing multiple flight positions; determining a first mode coefficient based on the aerial position model and the current operating mode of the detection drone, determining a second mode coefficient based on the aerial position model and the flight trajectory of the intrusion drone, and determining the corresponding countermeasure mode based on the first mode coefficient, the second mode coefficient, and the countermeasure mode mapping relationship. This countermeasure mode covers the corresponding countermeasure signal and countermeasure range. If the intrusion drone leaves the countermeasure range, the detection drone will be triggered to follow the countermeasure mode relative to the intrusion drone based on the direction of departure of the intrusion drone, until the intrusion drone is in a malfunctioning state. The detection drone uses visual detection to capture the fall trajectory of the intrusion drone. Based on the fall trajectory and the orientation of the detection drone's lights, a warning aperture is determined. This includes: after the intrusion drone malfunctions and descends, unable to fly, the detection drone uses visual detection to capture multiple landing positions of the intrusion drone and determines its fall trajectory based on these positions, the current wind direction, and the drone's weight; predicting the drone's landing position relative to the ground based on its fall trajectory, and determining the light's orientation command based on this landing position, the detection drone's attitude, and the orientation of the lights mounted on the detection drone. The warning aperture is determined based on the directional command of the light body, the light emission range of the light body, and the light emission mode of the light body. The drop location is within the warning aperture. The drop trajectory prediction is a dynamic process because the position and speed of the intruding drone will change continuously as it descends. The drop location is a two-dimensional coordinate (X,Y) representing its specific location on the ground. The warning aperture is a circular or elliptical area with a certain radius centered on the predicted drop location.

2. The method for countering unmanned aerial vehicles according to claim 1, characterized in that, The process of determining aerial images based on visual detection by the detection drone includes: During the flight of the detection drone, the camera parameters of multiple cameras are determined based on the drone's flight speed, environmental parameters of the drone's environment, and the mapping relationship of camera parameters; multiple cameras are mounted on the detection drone and at different positions on the drone. Multiple sub-images are acquired based on the camera parameters of multiple cameras, with the detection drone as the center. These sub-images correspond to images of different directions of the detection drone body, and an aerial image is obtained by stitching together the multiple sub-images.

3. The method for countering unmanned aerial vehicles according to claim 1, characterized in that, The step of determining the aerial position model of the detection drone relative to the intruding drone based on aerial imagery and distance data detected by detection sensors includes: The shape of the intrusion drone is determined based on aerial images, and the direction of the detection drone relative to the intrusion drone is determined based on the shape of the intrusion drone, the position of the intrusion drone and the position of the detection drone. The flight direction of the detection drone is adjusted along this direction so that the head of the detection drone faces the intrusion drone. The detection sensor is positioned in front of the detection drone to acquire distance data between the detection drone and the intrusion drone, and to 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 method for countering unmanned aerial vehicles according to claim 1, characterized in that, The determination of countermeasure signals 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 drone and the intrusion drone is updated in real time. If the relative distance is greater than the preset relative distance threshold, the flight speed of the detection drone is adjusted according to the flight speed of the intrusion drone and the relative distance, so that the relative distance is dynamically maintained within the preset relative distance threshold.

5. The method for countering unmanned aerial vehicles according to claim 1, characterized in that, If the intruding drone leaves the countermeasure range, the detection drone will be triggered in a follow-up countermeasure mode relative to the intruding drone based on the direction of departure, until the intruding drone is rendered inoperable, including: When the intruding drone is countered by the detection drone, the intruding drone further increases its flight speed to escape the countermeasure range. At this time, the departure position of the intruding drone is collected based on the visual detection of the detection drone.

6. The method for countering unmanned aerial vehicles according to claim 1, characterized in that, If the intruding drone leaves the countermeasure range, the detection drone will be triggered in a follow-up countermeasure mode relative to the intruding drone based on the direction of departure of the intruding drone, until the intruding drone is in a malfunctioning state. This also includes: Based on the departure position of the intrusion drone and the flight direction of the detection drone, the direction adjustment command of the detection drone is determined, and the flight turn of the detection drone is triggered according to the direction adjustment command, so that the detection drone continues to face the intrusion drone and triggers the follow-up countermeasure mode of the detection drone relative to the intrusion drone. In this follow-up countermeasure mode, the detection drone flies in response to the intrusion drone and further expands the countermeasure range according to the parameters of the detection drone until the intrusion drone is successfully countered by the detection drone and the intrusion drone is in a malfunctioning state.

7. A drone countermeasure system based on detection sensors, characterized in that, The drone countermeasure system based on the detection sensor is used to implement the drone countermeasure method based on the detection sensor as described in any one of claims 1-6, wherein the drone countermeasure system based on the detection sensor includes: An aerial image module is used to determine aerial images based on visual detection of the reconnaissance drone; An aerial position module is used to determine the aerial position model of the detection drone relative to the intruding drone based on aerial imagery and distance data detected by detection sensors. The countermeasure module is used to determine 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; The follow-up module is used to trigger the follow-up countermeasure mode of the detection drone relative to the intrusion drone based on the direction of the intrusion drone's departure if the intrusion drone leaves the countermeasure range, until the intrusion drone is in a malfunctioning state. The warning aperture module is used to detect drones by visually capturing the fall trajectory of intruding drones and to determine the warning aperture based on the fall trajectory of the intruding drone and the orientation of the detection drone's lights.

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