Unmanned aerial vehicle navigation decoy system

Abstract

The invention relates to the field of anti-unmanned aerial vehicles, in particular to an unmanned aerial vehicle navigation decoy system. The real-time state of the target unmanned aerial vehicle is obtained through the sensing and tracking module, the confidence coefficient of a satellite navigation signal obtained by the target unmanned aerial vehicle is reduced through the satellite signal interference module, the expected decoy trajectory is generated through the trajectory generation module, and the geometric parameters and the refreshing sequence of the visual landmark interference pattern are updated in real time based on the expected decoy trajectory. And the laser projection interference array responds to the decoy control module and dynamically projects a visual landmark interference pattern with a specific code in the downward-looking imaging view field of the target unmanned aerial vehicle when the satellite signal interference does not take effect. Interference on the visual navigation system of the unmanned aerial vehicle is achieved through the visual landmark interference pattern, the problem that a traditional satellite navigation signal deception means is invalid when the unmanned aerial vehicle has the flying capacity under the condition of no satellite signal is solved, and the deception success rate is increased.

Description

Technical Field

[0001] This invention relates to the field of anti-drone technology, and more particularly to a drone navigation deception system. Background Technology

[0002] In recent years, with the rapid development of drone technology, the number of drones in use has grown rapidly. This has led to a sharp increase in the pressure on low-altitude security control. In sensitive airspace such as civil aviation airport airspace protection zones, national defense and military bases, and venues for major events, illegal drone intrusion, aerial photography and mapping, and logistics infiltration have posed a substantial threat to air defense security, privacy protection, and the operation of critical facilities. To address these challenges, current counter-drone technology systems are developing along two main paths: one is the "hard kill" approach centered on physical interception, including active defense measures such as radio frequency interference forcing landings, capture net launchers, and directed energy weapons; the other is the more widely applicable "soft kill" approach based on satellite navigation signal deception, which interferes with drone positioning systems by generating misleading navigation signals.

[0003] For example, Chinese Patent Publication No. CN115096141A discloses a platform-based method and system for countering unmanned aerial vehicles (UAVs). Based on the detection data from the radio frequency (RF) detection module, it determines whether to activate the video monitoring module to identify the UAV in the air. If the RF detection module and the video monitoring module confirm the presence of a UAV, it activates the RF jamming module to transmit RF jamming signals. It also activates the virtual satellite signal transmission module to intermittently transmit virtual satellite signals, causing the UAV to change its return route multiple times. Based on the intersection of the UAV's multiple return routes, it confirms the UAV's takeoff position. By coordinating multiple modules, including the RF detection module, video monitoring module, RF jamming module, and virtual satellite signal transmission module, it achieves countermeasures against UAVs. Compared to relying on manual operation of individual functional modules, this avoids failures due to human error and provides a better UAV countermeasure capability.

[0004] It is evident that the following problems still exist in the existing technology. 1. In the existing technology, the further interference of the drone's ability to fly without satellite signal is not considered. With the development of drone technology, blind flight or autonomous flight without satellite signal has become a common function of mid-to-high-end consumer drones. Traditional satellite navigation signal deception methods will be ineffective against drones with the ability to fly without satellite signal. 2. In practice, satellite navigation signal spoofing may interfere with other nearby devices that have satellite navigation capabilities. In some scenarios, a high level of interference signal strength cannot be used, resulting in a low success rate of navigation signal interference. Summary of the Invention

[0005] To address this, the present invention provides a drone navigation deception system to overcome the problems in the prior art where traditional satellite navigation signal deception methods fail when targeting drones capable of flying without satellite signals, and where large interference signal strength cannot be used in some scenarios, resulting in a low success rate of navigation signal interference.

[0006] To achieve the above objectives, the present invention provides a drone navigation deception system, comprising: The perception and tracking module is used to obtain the real-time status of the target drone, including the drone's spatial position, speed, and attitude. The satellite signal jamming module is used to generate a controllable suppressive jamming signal at the satellite navigation signal receiving frequency of the target UAV, and to generate a predicted flight path for the UAV based on the real-time status of the UAV in order to determine the risk of intrusion. The trajectory generation module, which is connected to the perception and tracking module, is used to generate several desired decoy trajectories based on the real-time status of the target UAV, traverse the pattern templates corresponding to each desired decoy trajectory, filter the target desired decoy trajectory based on the differences in image features between each pattern template, and update the geometric parameters and template projection position of the visual landmark interference pattern in real time based on the corresponding pattern template. The decoy control module is connected to the satellite signal jamming module. It is used to determine whether the satellite signal jamming is effective based on the real-time status of the target UAV obtained by the perception and tracking module, so as to adaptively switch the decoy mode. The laser projection jamming array, which responds to the deception control module, consists of several sets of servo gimbals and laser projection units mounted on the servo gimbals, and is used to dynamically project visual landmark jamming patterns in the downward imaging field of view of the target UAV according to geometric parameters and refresh sequence.

[0007] Furthermore, the satellite signal jamming module, used to generate controllable suppression jamming signals at the satellite navigation signal receiving frequency of the target UAV, includes: The system dynamically scans the target UAV's communication frequency band and locks onto the satellite navigation frequency, generating a noise interference waveform with adjustable bandwidth. It then outputs a directional interference beam through a power amplifier to cover the target airspace where the UAV is located.

[0008] Furthermore, the trajectory generation module is used to generate several desired decoy trajectories based on the real-time state of the target UAV, including: It is used to generate several continuous and smooth desired decoy trajectories that satisfy preset steering angle constraints based on the real-time status of the target UAV.

[0009] Furthermore, the satellite signal interference module determines the intrusion risk by including: Based on the spatial location of the target UAV and the predicted flight path, the relative positional relationship between the predicted flight path and the preset safe area is determined to assess the intrusion risk.

[0010] Furthermore, the trajectory generation module is used to traverse the pattern templates corresponding to each of the desired deception trajectories, including: Determine the projection trajectory of each of the desired deception trajectories onto the reference plane, and determine the region where the projection trajectory is located; Pattern templates are determined based on regions; In this process, the association between each region and the pattern template is pre-established.

[0011] Furthermore, the trajectory generation module is used to filter the target desired decoy trajectory based on the differences in image features between each of the pattern templates, including: For each desired deception trajectory, calculate the environmental chromaticity deviation and edge gradient interference intensity of the pattern template. Based on the environmental chromaticity deviation and the edge gradient interference intensity, the comprehensive evaluation value corresponding to each expected deception trajectory is calculated. Candidate expected deception trajectories with comprehensive evaluation values ​​less than the expected threshold are selected and a set of candidate expected deception trajectories is constructed. The candidate expected decoy trajectory with the shortest total flight distance in the set of candidate expected decoy trajectories is taken as the target expected decoy trajectory.

[0012] Furthermore, the trajectory generation module is used to update the projection sequence and projection position of the visual landmark interference pattern in real time based on the corresponding pattern template, including: This is used to determine the order of image templates based on the flight sequence, thus obtaining the projection sequence; This is used to determine the drone's pre-path area based on the drone's real-time status, and the center of the pre-path area is determined as the projection position.

[0013] Furthermore, the deception control module is used to determine whether satellite signal interference is effective based on the real-time status of the target UAV obtained by the perception and tracking module, so as to adaptively switch the deception mode, including, After the satellite signal jamming module generates a controllable suppressive jamming signal, it compares the rate of change of the spatial distance between the real-time position of the UAV and the boundary of the preset safe zone. If the rate of change of spatial distance is greater than the preset index and the distance from the boundary of the safe zone is less than the distance dynamic threshold, it is determined that the satellite signal jamming has not taken effect. The distance dynamic threshold is dynamically adjusted based on the rate of change of spatial distance.

[0014] Furthermore, the deception control module adaptively switches deception modes, including: If it is determined that the satellite signal interference is ineffective, the visual deception mode is activated, and the laser projection jamming array is controlled to start working.

[0015] Furthermore, the laser projection jamming array is used to dynamically project visual landmark jamming patterns within the downward-looking imaging field of view of the target UAV according to geometric parameters and a refresh sequence, including: Used to sequentially call image templates according to the projection sequence, and project visual landmark interference patterns at the determined projection positions; Among them, the visual landmark interference pattern is generated by adding obstacle targets based on the corresponding image template.

[0016] Compared with existing technologies, this invention acquires the real-time status of the target UAV through a perception and tracking module, reduces the target UAV's confidence in satellite navigation signals through a satellite signal jamming module, generates a desired deception trajectory through a trajectory, and updates the geometric parameters and refresh sequence of the visual landmark interference pattern in real time based on this trajectory. A laser projection jamming array responds to the deception control module by dynamically projecting a specifically encoded visual landmark interference pattern into the target UAV's downward-facing imaging field of view when satellite signal jamming is ineffective. By interfering with the UAV's visual navigation system through the visual landmark interference pattern, this invention effectively counters UAVs capable of flying in satellite signal denial environments, overcomes the failure risk of traditional satellite navigation signal deception methods in such scenarios, and significantly improves the deception success rate.

[0017] In particular, this invention considers the control principles of UAVs in low-quality satellite navigation signal environments. When satellite positioning quality falls below a threshold, mainstream UAV platforms automatically switch to a vision-driven navigation mode. This invention projects visual landmark interference patterns corresponding to the decoy trajectory using a laser projection interference array. By leveraging the pattern recognition characteristics of the UAV's visual navigation system, it forces the UAV to convert false landmark information into spatial position parameters, thereby maintaining decoy continuity even in satellite denial environments.

[0018] In particular, this invention considers projecting visual landmark interference patterns based on preset image templates. These visual landmark interference patterns are generated by adding virtual obstacle targets to the preset image templates to induce drones to avoid obstacles or change direction. For example, by adding an obstacle pattern to the left of a preset flight path, the drone can be induced to turn right to avoid obstacles, thereby achieving precise control over the drone's flight trajectory. This system, through the flexible combination of different image templates and inducing elements, can adapt to various deception scenarios and drone models, significantly improving the accuracy and reliability of visual navigation interference.

[0019] In particular, this invention continuously collects and analyzes the spatial position, velocity, and attitude data of the target UAV through a perception and tracking module, thereby achieving real-time evaluation of the effectiveness of the satellite signal jamming module. After the satellite signal jamming module generates a controllable suppressive jamming signal, the system continuously compares the rate of change of the spatial distance between the UAV's real-time position and the boundary of the preset safe zone; if the rate of change is greater than a preset indicator and the current distance is less than a dynamic distance threshold, it is determined that the satellite signal jamming has not taken effect, that is, the suppressive jamming has failed to effectively change the UAV's trajectory.

[0020] In particular, the dynamic distance threshold used in this invention is not a fixed value, but is dynamically set and updated based on the rate of change of spatial distance. This allows for adaptive adjustment of the warning range to different intrusion speeds, improving the system's response capability to high-speed approaching targets. In practical applications, even in the initial stage of satellite navigation signal deception, UAVs may maintain their original flight state briefly due to inertia. The dynamic threshold-based judgment mechanism can effectively avoid false alarms or missed alarms caused by fixed threshold settings, enhancing the system's discrimination accuracy and operational reliability in complex interference environments. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure of an embodiment of the invention; Figure 2 The following is a logic block diagram for determining a suspected low-altitude signal as an unmanned aerial vehicle (UAV) according to an embodiment of the invention. Figure 3 A logic block diagram for selecting candidate desired deception trajectories with a comprehensive evaluation value less than the expected threshold, as shown in the embodiment of the invention. Figure 4 This is a logic block diagram illustrating how satellite signal interference is determined to be ineffective in an embodiment of the invention. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "connected" or "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] Please see Figure 1 As shown, it is a structural schematic diagram of an embodiment of the present invention. A drone navigation deception system of the present invention includes: The perception and tracking module is used to obtain the real-time status of the target drone, including the drone's spatial position, speed, and attitude. The satellite signal jamming module is used to generate a controllable suppressive jamming signal at the satellite navigation signal receiving frequency of the target UAV, and to generate a predicted flight path for the UAV based on the real-time status of the UAV in order to determine the risk of intrusion. The trajectory generation module, which is connected to the perception and tracking module, is used to generate several desired decoy trajectories based on the real-time status of the target UAV, traverse the pattern templates corresponding to each desired decoy trajectory, filter the target desired decoy trajectory based on the differences in image features between each pattern template, and update the geometric parameters and template projection position of the visual landmark interference pattern in real time based on the corresponding pattern template. The decoy control module is connected to the satellite signal jamming module. It is used to determine whether the satellite signal jamming is effective based on the real-time status of the target UAV obtained by the perception and tracking module, so as to adaptively switch the decoy mode. The laser projection jamming array, which responds to the deception control module, consists of several sets of servo gimbals and laser projection units mounted on the servo gimbals, and is used to dynamically project visual landmark jamming patterns in the downward imaging field of view of the target UAV according to geometric parameters and refresh sequence.

[0026] Specifically, the perception and tracking module is used to acquire the real-time status of the target drone, including... The phased array radar identifies low-altitude suspected signals entering the warning area, detects their flight altitude, distance, and speed, and determines the spatial location of the UAV. The image of the nearest visual detection unit is retrieved to determine whether the suspected low-altitude signal is a drone; If the suspected low-altitude signal is a drone, then identify the drone's attitude.

[0027] Specifically, the image of the visual inspection unit can be an optoelectronic ball containing a visible light camera and an infrared thermal imager, or other devices with image acquisition capabilities and zoom functions.

[0028] Specifically, the image of the nearest visual detection unit is retrieved to determine whether the suspected low-altitude signal belongs to a drone. The YOLOv5 model is used to segment suspected targets in images to perform target segmentation on visible light or infrared images and extract the contours of suspected targets. Please see Figure 2 As shown, this is a logic block diagram for determining whether a low-altitude suspected signal is a drone according to an embodiment of the present invention. If any drone feature is detected in the outline of the target, the low-altitude suspected signal is determined to be a drone. Drone features include, but are not limited to, rotors, wings, landing gear, onboard gimbals, and arms.

[0029] Specifically, the attitude of the UAV, including pitch angle, roll angle, and heading angle, is identified by continuous frame images collected by the visual detection unit.

[0030] Specifically, the satellite signal jamming module is used to generate controllable suppression jamming signals at the satellite navigation signal receiving frequency of the target UAV, including: The system dynamically scans the target UAV's communication frequency band and locks onto the satellite navigation frequency, generating a noise interference waveform with adjustable bandwidth. It then outputs a directional interference beam through a power amplifier to cover the target airspace where the UAV is located.

[0031] Specifically, the trajectory generation module is used to generate several desired decoy trajectories based on the real-time status of the target UAV, including: It is used to generate several continuous and smooth desired decoy trajectories that satisfy preset steering angle constraints based on the real-time status of the target UAV.

[0032] Specifically, based on the real-time status of the UAV, including its current spatial position, velocity vector, and attitude information, the system predicts and extrapolates the UAV's flight trajectory over a future period based on its current flight status, thereby generating a continuous and smooth predicted flight path. The time range for this prediction and extrapolation is a preset value, and in this embodiment of the invention, the time range is [5 seconds, 8 seconds].

[0033] The predicted flight path is projected onto a top-view view of a preset safe area, and the distance between the boundary of the preset safe area and the projected predicted flight path is calculated. The distance is defined as positive outside the safe area and negative inside the safe area. If the distance is consistently positive, it indicates that flying along the original route will not intrude. If negative values ​​exist, a parametric curve algorithm is used to generate several continuous and smooth desired decoy trajectories that satisfy preset steering angle constraints.

[0034] Specifically, starting from the current location of the drone and ending at a virtual waypoint 10 meters outside the boundary of the warning area, 10-20 desired deception trajectories are generated between these two points using parametric curves.

[0035] Specifically, the Dubins curve or Clothoid curve, which are commonly used in engineering, can be used as the parameterized curve.

[0036] Understandably, each curve should satisfy: The curve is kept at a positive distance from the boundary of the preset safe zone throughout its entire length to prevent drones from intruding into the safe zone.

[0037] The curve curvature meets the preset steering angle constraint to avoid the curve curvature exceeding the drone's turning capability.

[0038] Specifically, the satellite signal interference module determines the intrusion risk by including: Based on the spatial location of the target UAV and the predicted flight path, the relative positional relationship between the predicted flight path and the preset safe area is determined to assess the intrusion risk.

[0039] If the predicted flight path will enter a preset safe zone, the target drone is deemed to pose an intrusion risk.

[0040] Specifically, the trajectory generation module is used to traverse the pattern templates corresponding to each of the desired deception trajectories, including: Determine the projection trajectory of each of the desired deception trajectories onto the reference plane, and determine the region where the projection trajectory is located; Pattern templates are determined based on regions; In this process, the association between each region and the pattern template is pre-established.

[0041] Specifically, the trajectory generation module is used to filter the target desired decoy trajectory based on the differences in image features between each of the pattern templates, including: For each desired deception trajectory, calculate the environmental chromaticity deviation and edge gradient interference intensity of the pattern template. Based on the environmental chromaticity deviation and the edge gradient interference intensity, the comprehensive evaluation value corresponding to each expected deception trajectory is calculated. Candidate expected deception trajectories with comprehensive evaluation values ​​less than the expected threshold are selected and a set of candidate expected deception trajectories is constructed. The candidate expected decoy trajectory with the shortest total flight distance in the set of candidate expected decoy trajectories is taken as the target expected decoy trajectory.

[0042] Specifically, the weighted sum of the environmental chromaticity deviation and the edge gradient interference intensity is used as the comprehensive evaluation value corresponding to the desired deception trajectory.

[0043] Specifically, the environmental chromaticity deviation is obtained by comparing the average color of the pattern template corresponding to the desired deception trajectory with the average color of the ground background at the same time. First, the red, green, and blue values ​​of all pixels in the template area are averaged, and then the red, green, and blue values ​​of the ground background are subtracted. The three differences are summed to obtain the total difference. The smaller the total difference, the closer the pattern is to the original terrain features, and the easier it is to successfully deceive the drone and misjudge it as real terrain features.

[0044] Specifically, the chromaticity deviation standard value was determined through several consecutive drone intrusion tests conducted at the installation site before the system was officially put into use. The average of the total differences in the expected trolley trajectories of successfully lured drones during the tests was taken as the chromaticity deviation standard value.

[0045] Specifically, the edge gradient interference intensity is determined by first converting the pattern template corresponding to the desired deception trajectory into a grayscale image, then using the Sobel operator to obtain the gradient magnitude of each pixel, and then averaging all gradient magnitudes within the template region to obtain the average gradient value. Subsequently, the ratio of this average gradient value to the standard gradient value is taken as the edge gradient interference intensity.

[0046] Specifically, the gradient standard value is calculated by conducting several drone intrusion tests at the same installation site before the system is officially used. The average gradient value is calculated by converting the pattern templates corresponding to all successful deception trajectories in the tests to grayscale. Then, 0.9 times this average value is used as the gradient standard value, thereby ensuring that effective interference can be maintained under most lighting and ground texture conditions on site.

[0047] Specifically, the weighted sum of the ambient chromaticity deviation and the edge gradient interference intensity is used as the comprehensive evaluation value. The weighting weight for the ambient chromaticity deviation is 0.6, and the weighting weight for the edge gradient interference intensity is 0.4.

[0048] Specifically, the expected threshold is determined by conducting at least twenty complete decoy flight tests in the actual field before the system's formal deployment. The comprehensive evaluation value corresponding to the expected decoy trajectory of successfully luring the drone in each test is recorded. The arithmetic average of these values ​​is then increased by 10%, and the resulting value is the expected threshold used in the field. This threshold retains sufficient margin to cover environmental fluctuations while ensuring that trajectories below this threshold are promptly filtered out.

[0049] Please see Figure 3 As shown, Figure 3 The logic diagram for filtering out candidate expected deception trajectories whose comprehensive evaluation value is less than the expected threshold is as follows: if the comprehensive evaluation value is less than the expected threshold, then the candidate expected deception trajectories are filtered out and added to the candidate expected deception trajectory set.

[0050] Specifically, the trajectory generation module is used to update the projection sequence and projection position of the visual landmark interference pattern in real time based on the corresponding pattern template, including: This is used to determine the order of image templates based on the flight sequence, thus obtaining the projection sequence; This is used to determine the drone's pre-path area based on the drone's real-time status, and the center of the pre-path area is determined as the projection position.

[0051] Specifically, the image templates are pre-collected images of the real terrain surrounding the application scenario, and are updated regularly based on changes in season, lighting, or surface vegetation cover. Image templates can be acquired through drone aerial photography or ground-based acquisition equipment to ensure their authenticity and timeliness. All image templates undergo geometric correction and registration before being stored in the database to ensure they have uniform spatial resolution, pixel size, and geographic orientation. During application, when the system calls an image template, it must strictly ensure that its projected size, spatial orientation, and heading angle are consistent with the parameters of the current deception scenario to maintain the spatial consistency of the visual landmark interference pattern and the effectiveness of the deception.

[0052] Specifically, the system determines the preset areas corresponding to each spatial location along the desired decoy trajectory based on its flight sequence, and then calls up image templates associated with each area accordingly. This generates a template calling sequence, i.e., a projection sequence, that matches the flight sequence. This process ensures that the projection sequence of the visual landmark interference pattern is strictly synchronized with the spatial advancement of the decoy trajectory, guaranteeing the temporal and spatial continuity and consistency of the visual navigation interference.

[0053] Specifically, the deception control module is used to determine whether satellite signal interference is effective based on the real-time status of the target UAV obtained by the perception and tracking module, and to adaptively switch the deception mode, including: Please see Figure 4 As shown, Figure 4 The following is a logic block diagram for determining that satellite signal interference is ineffective in an embodiment of the invention. After the satellite signal interference module generates a controllable suppressive interference signal, it compares the rate of change of the spatial distance between the real-time position of the UAV and the boundary of the preset safe zone. If the rate of change of the spatial distance is greater than the preset index and the distance from the boundary of the safe zone is less than the distance dynamic threshold, then it is determined that the satellite signal interference is ineffective. The distance dynamic threshold is dynamically adjusted based on the rate of change of spatial distance.

[0054] Specifically, the distance dynamic threshold is dynamically adjusted based on the rate of change of spatial distance.

[0055] Specifically, the distance dynamic threshold is the product of the average rate of change and the warning time; the average rate of change is the average of the rate of change of the UAV's spatial distance within a preset time period after the controllable suppression jamming signal is activated. The preset time period length is in the range of [0.5 seconds, 1.5 seconds], and the start time of the preset time period and the activation time of the suppression jamming signal are in the range of [1.5 seconds, 2.5 seconds].

[0056] It is understandable that the warning time is related to the distance between the boundary of the warning area and the boundary of the safe area in the actual application scenario. In this embodiment of the invention, for scenarios where the distance between the boundaries is greater than 50 meters, the warning time is recommended to be no less than 2 seconds. Those skilled in the art can make the optimal selection of the warning time for the actual use scenario, which will not be elaborated here.

[0057] Specifically, the deception control module adaptively switches deception modes, including: After determining that the satellite signal interference has not taken effect, the deception control module immediately activates the visual deception mode and controls the laser projection jamming array to start working.

[0058] Specifically, the laser projection jamming array is used to dynamically project visual landmark jamming patterns into the downward-facing imaging field of view of the target UAV according to geometric parameters and a refresh sequence, including, Used to sequentially call image templates according to the projection sequence, and project visual landmark interference patterns at the determined projection positions; Among them, the visual landmark interference pattern is generated by adding obstacle targets based on the corresponding image template.

[0059] Specifically, visual landmark interference patterns are generated by adding obstacle targets to a corresponding image template. For example, virtual obstacle images can be added to the flight centerline and the left-side area of ​​the original image template to create a visual blocking effect. This pattern combination can induce the UAV's visual navigation system to identify false obstacles as real threats, thereby triggering its obstacle avoidance algorithm and controlling the UAV to turn right to avoid the preset "obstacles," ultimately achieving the goal of making it fly along the desired deception trajectory. By adding virtual obstacle images to the image template, it can be adapted to the visual perception capabilities of most UAV models, enhancing the accuracy and adaptability of deception control.

[0060] It is understood that virtual obstacle images can simulate the morphological features of buildings, trees, terrain protrusions, vehicles, or other typical ground obstacles. Those skilled in the art can construct virtual obstacle image databases according to actual conditions, which will not be elaborated upon here.

[0061] Specifically, when projecting a visual landmark interference pattern at the determined projection location, the system selects the geographically closest servo gimbal from the laser projection interference array based on the coordinates of the projection location. The servo gimbal then adjusts its attitude so that the laser projection unit mounted on it is precisely aligned with the target projection area.

[0062] Understandably, the installation positions of each servo gimbal in the laser projection jamming array should be as high as possible above the ground, such as on the top of a dedicated tower, the top of a building, or a high-altitude platform for tethering drones. This high-level deployment aims to expand the coverage of the optical projection, reduce the obstruction of the projection light path by ground obstacles, and ensure that the visual landmark jamming pattern can be projected completely and clearly onto the projection position, thereby ensuring its effectiveness and stability in jamming the drone's downward-looking imaging system.

[0063] Specifically, during the projection process, the laser projection unit monitors the ambient illuminance of the projection area in real time and automatically adjusts the laser power level and pattern contrast based on the ambient illuminance feedback value to ensure that visual landmark interference patterns are stably captured by the UAV downward imaging system under different lighting conditions.

[0064] Understandably, if the target drone, after being affected by visual landmark interference patterns, does not fly entirely along the intended decoy trajectory, but instead hovers, lingers, or changes direction, causing it to stop approaching the core protected area, this situation is still considered a partial decoy success. Such a response effectively halts or delays the drone's intrusion process, achieving the goal of preventing further intrusion into the secure area.

[0065] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A drone navigation deception system, characterized in that, include: The perception and tracking module is used to obtain the real-time status of the target drone. This includes the drone's spatial location, speed, and attitude. The satellite signal jamming module is used to generate a controllable suppressive jamming signal at the satellite navigation signal receiving frequency of the target UAV, and to generate a predicted flight path for the UAV based on the real-time status of the UAV in order to determine the risk of intrusion. The trajectory generation module, which is connected to the perception and tracking module, is used to generate several desired decoy trajectories based on the real-time status of the target UAV, traverse the pattern templates corresponding to each desired decoy trajectory, filter the target desired decoy trajectory based on the differences in image features between each pattern template, and update the geometric parameters and template projection position of the visual landmark interference pattern in real time based on the corresponding pattern template. The decoy control module is connected to the satellite signal jamming module. It is used to determine whether the satellite signal jamming is effective based on the real-time status of the target UAV obtained by the perception and tracking module, so as to adaptively switch the decoy mode. The laser projection jamming array, which responds to the deception control module, consists of several sets of servo gimbals and laser projection units mounted on the servo gimbals, and is used to dynamically project visual landmark jamming patterns in the downward imaging field of view of the target UAV according to geometric parameters and refresh sequence.

2. The drone navigation deception system according to claim 1, characterized in that, The satellite signal jamming module is used to generate controllable suppression jamming signals at the satellite navigation signal receiving frequency of the target UAV, including: The system dynamically scans the target UAV's communication frequency band and locks onto the satellite navigation frequency, generating a noise interference waveform with adjustable bandwidth. It then outputs a directional interference beam through a power amplifier to cover the target airspace where the UAV is located.

3. The drone navigation deception system according to claim 1, characterized in that, The trajectory generation module is used to generate several desired decoy trajectories based on the real-time status of the target UAV. include, It is used to generate several continuous and smooth desired decoy trajectories that satisfy preset steering angle constraints based on the real-time status of the target UAV.

4. The drone navigation deception system according to claim 1, characterized in that, The satellite signal interference module determines the risk of intrusion as follows: Based on the spatial location of the target UAV and the predicted flight path, the relative positional relationship between the predicted flight path and the preset safe area is determined to assess the intrusion risk.

5. The drone navigation deception system according to claim 1, characterized in that, The trajectory generation module is used to traverse the pattern templates corresponding to each of the desired deception trajectories, including: Determine the projection trajectory of each of the desired deception trajectories onto the reference plane, and determine the region where the projection trajectory is located; Pattern templates are determined based on regions; In this process, the association between each region and the pattern template is pre-established.

6. The drone navigation deception system according to claim 1, characterized in that, The trajectory generation module is used to filter the target desired deception trajectory based on the differences in image features between each of the pattern templates. For each desired deception trajectory, calculate the environmental chromaticity deviation and edge gradient interference intensity of the pattern template. Based on the environmental chromaticity deviation and the edge gradient interference intensity, the comprehensive evaluation value corresponding to each expected deception trajectory is calculated. Candidate expected deception trajectories with comprehensive evaluation values ​​less than the expected threshold are selected and a set of candidate expected deception trajectories is constructed. The candidate expected decoy trajectory with the shortest total flight distance in the set of candidate expected decoy trajectories is taken as the target expected decoy trajectory.

7. The drone navigation deception system according to claim 6, characterized in that, The trajectory generation module is used to update the projection sequence and projection position of the visual landmark interference pattern in real time based on the corresponding pattern template. This is used to determine the order of image templates based on the flight sequence, thus obtaining the projection sequence; This is used to determine the drone's pre-path area based on the drone's real-time status, and the center of the pre-path area is determined as the projection position.

8. The drone navigation deception system according to claim 1, characterized in that, The deception control module is used to determine whether satellite signal interference is effective based on the real-time status of the target UAV obtained by the perception and tracking module, and to adaptively switch the deception mode, including: After the satellite signal jamming module generates a controllable suppressive jamming signal, it compares the rate of change of the spatial distance between the real-time position of the UAV and the boundary of the preset safe zone. If the rate of change of spatial distance is greater than the preset index and the distance from the boundary of the safe zone is less than the distance dynamic threshold, it is determined that the satellite signal jamming has not taken effect. The distance dynamic threshold is dynamically adjusted based on the rate of change of spatial distance.

9. The drone navigation deception system according to claim 8, characterized in that, The deception control module adaptively switches between deception modes, including: If it is determined that the satellite signal interference is ineffective, the visual deception mode is activated, and the laser projection jamming array is controlled to start working.

10. The drone navigation deception system according to claim 1, characterized in that, The laser projection jamming array is used to dynamically project visual landmark jamming patterns into the downward-facing imaging field of view of the target UAV according to geometric parameters and refresh sequence, including, Used to sequentially call image templates according to the projection sequence, and project visual landmark interference patterns at the determined projection positions; Among them, the visual landmark interference pattern is generated by adding obstacle targets based on the corresponding image template.

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