Black flight unmanned aerial vehicle identification method

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a black flight unmanned aerial vehicle identification method, which comprises the following steps: a sight distance range detection step: detecting an unmanned aerial vehicle in a sight distance range through a visual sensor, positioning the position of the unmanned aerial vehicle based on a preset target detection algorithm, and establishing an unmanned aerial vehicle position list; a black flight unmanned aerial vehicle identification step: continuously receiving an ADS-B signal and a Remote ID signal through a pre-installed signal receiving device, and based on the position of the unmanned aerial vehicle and the ADS-B signal and the Remote ID signal, sequentially verifying whether the corresponding unmanned aerial vehicle sends the ADS-B signal and the Remote ID signal, whether the signal is a registration signal and whether the registration signal is a real signal in a sight distance range; and when any verification result is no, determining that the unmanned aerial vehicle is a black flight unmanned aerial vehicle. According to the invention, the position and the signal of the unmanned aerial vehicle are comprehensively analyzed and judged, and the reliability and the robustness of counterfeit signal identification are improved.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a method for identifying illegal drones. Background Art

[0002] Drone identification technology is mainly used to counter illegal flying, which refers to unregistered flying without a private pilot's license or a legal aircraft identity. This type of flying is dangerous and poses a serious threat to low-altitude traffic and public safety due to its characteristics of being out of the regulatory framework.

[0003] Its harmfulness lies in the following aspects: drones that do not send ADS-B signals and Remote ID signals cannot be identified by other aircraft or air traffic control systems, resulting in the risk of airspace collision, which poses a direct threat to aviation safety operations; there is also the possibility of sending forged Remote ID signals through devices to disguise themselves as legitimate drones, misleading the regulatory system, allowing attackers to exploit this vulnerability to cover up malicious activities; in addition, drones that deviate from the declared route may also pose a threat to public safety due to loss of control or operational errors, such as falling into crowded areas.

[0004] Existing identification methods use ADS-B or Remote ID to identify the identity of drones, but this method relies on drones actively broadcasting their identity information, cannot identify drones that deliberately turn off their signals, and are vulnerable to attacks from false and forged signals. Some identification methods also use ground-based radars, infrared sensors and other equipment to visually capture drones, but the resolution is low for small-sized, low-altitude drones, and they are easily blocked by terrain and clutter, and cannot be accurately captured in real time due to weather and light conditions.

[0005] In addition, the data from different monitoring and identification methods are not effectively integrated, resulting in information fragmentation. The deployment cost of high-precision detection equipment is too high, making it difficult to promote on a large scale. Summary of the invention

[0006] The embodiment of the present application provides a method for identifying illegal drones, so as to at least solve the problem of how to identify regular drones and various illegal drone behaviors within visual range in the related art.

[0007] The present application embodiment provides a method for identifying illegal drones, including: The line-of-sight range detection step detects drones within the line-of-sight range through visual sensors and locates the positions of the drones based on a preset target detection algorithm, and establishes a drone position list, wherein the visual sensor is mounted on an inspection drone or other inspection equipment that performs illegal flight inspection tasks in the target area.

[0008] The step of identifying illegal drones is to continuously receive ADS-B signals and Remote ID signals through a pre-installed signal receiving device, and based on the position of the drone and the ADS-B signal and Remote ID signal, verify in turn whether the corresponding drone within the line of sight sends ADS-B signals and Remote ID signals, whether the signal is a registered signal, and whether the registered signal is a real signal. When any verification result is no, it is judged to be an illegal drone, wherein the ADS-B signal includes first position information, altitude information, speed information, heading information and ICAO address, and the Remote ID signal includes the serial number of the drone, second position information, operator position, and status information.

[0009] In some embodiments, the step of identifying illegal drones includes: A signal existence judgment step is to parse the ADS-B signal and Remote ID signal, obtain the location information in the ADS-B signal and / or Remote ID signal, compare it with the drone location list, identify the drone that does not send a signal as an illegal drone, and the drone that sends a signal enters the registration status judgment step for registration verification; The registration status judgment step is to parse the ICAO address in the ADS-B signal and / or the serial number in the Remote ID signal, perform real-time query verification in the drone supervision database, identify the drone sending the unregistered signal as an illegal drone, and the drone sending the registered signal enters the real signal judgment step for authenticity verification; The real signal judgment step performs frequency characteristic analysis, signal strength analysis and time-space consistency analysis on the ADS-B signal and Remote ID signal respectively, verifies the similarity between the signal and the normal signal, verifies the matching degree between the signal strength and the distance, and verifies the consistency of the signal strength change, the position change of the UAV, and the signal frequency change with the movement law of the aerial vehicle, and comprehensively judges whether the signal is a real signal or a forged signal based on the verification results of the ADS-B signal and the Remote ID signal, and identifies the UAV with forged signals as an illegal UAV. If any one of them is judged to be a forged signal, it is identified as an illegal UAV.

[0010] The embodiment of the present application performs comprehensive judgment by performing spectrum analysis, signal strength analysis, and spatiotemporal consistency analysis on ADS-B signals and Remote ID signals, thereby improving the reliability and robustness of identifying counterfeit signals and avoiding missed judgments or misjudgments that may occur in single signal analysis.

[0011] In some embodiments, the real signal determination step further comprises: Spectrum analysis step, receiving the signal to be verified , perform fast Fourier transform on the signal to be verified to obtain the spectrum of the signal to be verified , and then normalize it to calculate the spectrum template of the normal signal The cosine similarity and Euclidean distance of the spectrum are combined to determine whether it is an abnormal spectrum, and if so, it is a forged signal; The signal strength analysis step is to obtain the position of the target drone in the drone position list, determine the distance between the target drone and the signal receiving device, calculate the corresponding received power based on the distance using a free space path loss model, and determine whether the received power is consistent with the actual power of the signal to be verified. If not, it is a forged signal; Spatiotemporal consistency analysis step, continuously obtaining the location of the target drone and the signal to be verified , analyze whether the signal strength change during its movement conforms to the free space path loss model, whether the speed change is smooth, and analyze whether the signal frequency offset of the signal to be verified is consistent with expectations. If any of the analysis results is no, it is a fake signal.

[0012] In some embodiments, the step of identifying illegal drones further includes: In the abnormal flight judgment step, for the drone identified as a real signal in the real signal judgment step, the heading information in its ADS-B signal is analyzed, and its position in the drone position list is judged to be compliant, that is, consistent with its preset route. If not, it is judged to be an illegal drone.

[0013] In some embodiments, in the spectrum analysis step, the abnormal spectrum is a signal spectrum whose cosine similarity is lower than a first threshold and whose Euclidean distance exceeds a second threshold; wherein the cosine similarity is expressed as: ; The Euclidean distance is expressed as: .

[0014] In some embodiments, in the signal strength analysis step, the received power is represented by the following calculation model: , in, is the transmission power, and are the transmitting antenna gain and the receiving antenna gain, respectively. For distance, is the frequency of the normal signal, .

[0015] In some embodiments, the spatiotemporal consistency analysis step further comprises: The signal strength consistency analysis step is to calculate the expected received power at the current position of the target UAV based on the free space path loss model, determine the actual received power at the current position based on the signal to be verified, and calculate the mean square error between the actual received power and the expected received power. If the error is greater than a set error value, the signal is judged to be abnormal. The position consistency analysis step is to calculate the moving speed and moving acceleration of the target UAV according to its position, and if the moving acceleration is greater than an acceleration threshold, it is determined that the signal is abnormal; The frequency consistency analysis step measures the signal frequency of the signal to be verified and calculates the absolute difference between the actual frequency offset and the expected frequency offset based on its position. If the absolute difference is greater than the offset threshold, the signal is judged to be abnormal.

[0016] In some embodiments, in the signal strength consistency analysis step, the current distance between the target drone and the signal receiving device at the current position is calculated, and the current distance is substituted into the calculation model of the received power to obtain the expected received power. , combined with the actual receiving power of the signal receiving device The mean square error (MSE) is calculated as: .

[0017] In some of the embodiments, the first acceleration threshold is configured as the maximum acceleration of the drone. If the absolute value of the movement acceleration is greater than the acceleration threshold, it is determined that the position change of the target drone is not smooth and there is a signal abnormality.

[0018] In some embodiments, in the frequency consistency analysis step, the signal frequency is expressed as , the expected frequency offset is calculated based on the following calculation model: ,in is the moving speed, is the angle between the target UAV's motion direction vector and the visual sensor's sight line direction, where the visual sensor points to the target UAV; The actual frequency offset is calculated based on the following calculation model: .

[0019] Based on the above steps, the embodiment of the present application determines whether the signal conforms to the physical motion laws of the aerial vehicle based on the signal strength, position, and frequency changes, thereby identifying the anomaly of the forged signal.

[0020] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a judgment logic diagram of a method for identifying illegal drones according to an embodiment of the present application; Figure 2 is a flow chart of a method for identifying illegal drones according to an embodiment of the present application; Figure 3 is a flow chart of step S2 of the method for identifying illegal drones according to an embodiment of the present application; Figure 4 A schematic diagram of the process of step S23 of the method for identifying illegal drones according to an embodiment of the present application; Figure 5 is a flow chart of step S233 of the method for identifying illegal drones according to an embodiment of the present application; Figure 6 is another flow chart of the method for identifying illegal drones according to an embodiment of the present application; Figure 7 Schematic diagram of the relative positions of drone A and drone B according to the method for identifying illegal drones in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0023] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0024] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantity limitation, and may indicate the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0025] The Automatic Dependent Surveillance - Broadcast (ADS-B) system is an aviation surveillance technology based on satellite navigation and digital communications. It is used to broadcast aircraft position, altitude, speed and other information in real time. It relies on the 1090MHz or 978MHz frequency and uses a specific protocol.

[0026] Remote ID is the drone's identification system, similar to an avionics license plate. It sends identity information, location information and flight status via wireless signal broadcast or the Internet for reception by ground equipment or other drones.

[0027] Although ADS-B and Remote ID signals have different functions, they are both important bases for drone identity authentication, and counterfeiters may counterfeit both separately. In scenarios such as civil aviation controlled airspace, drone logistics, and urban low-altitude traffic, high-precision airspace perception is required, and drones will be required to be equipped with both ADS-B and RemoteID. Based on this, this application designs a step-by-step screening method for identifying illegal drones.

[0028] Figure 1 is a logical schematic diagram of a method for identifying illegal drones according to an embodiment of the present application. Figures 2 to 6 is a flow chart of a method for identifying illegal drones according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps: In the line-of-sight range detection step S1, the visual sensor is used to detect the drones within the line-of-sight range and locate the drones based on a preset target detection algorithm, and a drone position list is established, wherein the visual sensor is mounted on the inspection drones or other inspection equipment that perform illegal flight inspection tasks in the target area. The visual sensor includes but is not limited to a binocular camera or a depth camera. The model is trained based on the image data set collected within the line-of-sight range of the drone. The trained target detection algorithm is used to obtain the real-time video stream detected by the visual sensor, and output the bounding box, category, and image coordinates of the drone. , convert the image coordinates of each drone into the camera coordinate system and then into the world coordinate system to obtain the position of the drone. Specifically, When using a binocular camera or a depth camera for detection, the camera's intrinsic parameter matrix and the center point of the detected drone's bounding box are used to convert points in the image coordinate system to the camera coordinate system through depth information or parallax. Based on the known current inspection drone position, posture, and camera installation position, the position of the target drone is further calculated and located.

[0029] When using a monocular camera for detection, the wing width of the target drone can be obtained based on the drone category. The distance D between the target drone and the current inspection drone can be calculated through the pixel width in the image. The azimuth and pitch angles of the target drone in the camera coordinate system are calculated through the target key coordinates in the image and the camera internal parameters. The three-dimensional coordinates are then calculated in combination with the distance D and finally converted to the world coordinate system.

[0030] The default target detection algorithm uses the CenterNet target detection network. This embodiment changes its backbone network structure to UNet, which is suitable for feature extraction and upsampling and helps capture small targets. Center detection loss and bounding box regression loss are introduced to deal with the imbalance of positive and negative samples in small target detection. At the same time, auxiliary branches are added during training and discarded during inference to improve model training accuracy and inference speed.

[0031] In step S2 of identifying illegal drones, the ADS-B signal and Remote ID signal are continuously received by a pre-installed signal receiving device. Based on the position of the drone and the ADS-B signal and Remote ID signal, it is verified in turn whether the corresponding drone within the line of sight sends an ADS-B signal and a Remote ID signal, whether the signal is a registered signal, and whether the registered signal is a real signal. If any verification result is no, it is judged to be an illegal drone, wherein the ADS-B signal includes first position information, altitude information, speed information, heading information and ICAO address, and the Remote ID signal includes the serial number of the drone, second position information, operator position, and status information.

[0032] refer to Figure 3 As shown, the illegal drone identification step S2 includes: Signal presence judgment step S21, parsing the ADS-B signal and the Remote ID signal, obtaining the location information in the ADS-B signal and / or the Remote ID signal, comparing it with the drone location list, identifying the drone that does not send a signal as an illegal drone, and the drone that sends a signal enters the registration status judgment step for registration verification; The registration status determination step S22 is to parse the ICAO address in the ADS-B signal and / or the serial number in the Remote ID signal, perform real-time query verification in the drone supervision database, identify the drone sending the unregistered signal as an illegal drone, and the drone sending the registered signal enters the real signal determination step for authenticity verification; In the real signal judgment step S23, the frequency characteristic analysis, signal strength analysis and time-space consistency analysis are performed on the ADS-B signal and the Remote ID signal respectively. By verifying the similarity between the signal and the normal signal, the matching degree between the signal strength and the distance, and the consistency between the signal strength change, the UAV position change, and the signal frequency change and the movement law of the aerial vehicle, the verification results of the ADS-B signal and the Remote ID signal are comprehensively used to determine whether the signal is a real signal or a forged signal, and the UAV with a forged signal is identified as an illegal UAV. If any one of them is determined to be a forged signal, it is identified as an illegal UAV.

[0033] In the embodiment of the present application, the verification result of the ADS-B signal and the Remote ID signal can be that both signals need to pass the registration verification and authenticity verification, or that only one of them can be considered not to be an illegal drone. The strictness of the verification result can be flexibly adjusted according to the control scenario. Among them, the illegal drone with a forged identity is the signal sent by low-cost equipment and ground equipment, disguised as a low-altitude drone. The embodiment of the present application performs spectrum analysis, signal strength analysis, and spatiotemporal consistency analysis on the ADS-B signal and the Remote ID signal for comprehensive judgment, thereby improving the reliability and robustness of identifying forged signals and avoiding possible missed judgments or misjudgments in single signal analysis.

[0034] In some of the above embodiments, reference Figure 4 As shown, the real signal determination step S23 further includes: Spectrum analysis step S231, receiving the signal to be verified , perform fast Fourier transform on the signal to be verified to obtain the spectrum of the signal to be verified , and then normalize it to calculate the spectrum template of the normal signal The cosine similarity and Euclidean distance of are combined to determine whether it is an abnormal spectrum. If so, it is a forged signal; wherein, , FFT is fast Fourier transform. In the spectrum analysis step S231, the abnormal spectrum is a signal spectrum whose cosine similarity is lower than the first threshold and whose Euclidean distance exceeds the second threshold. In this embodiment, the first threshold is set to 0.8 and the second threshold is set to 0.5, but it is not limited thereto. The cosine similarity is used to focus on the directional consistency of the spectrum shape, and the Euclidean distance is used to analyze the absolute difference of the spectrum amplitude, so as to accurately analyze the abnormal signal; wherein, the cosine similarity is expressed as: ; The Euclidean distance is expressed as: .

[0035] The above steps utilize normalization operation to eliminate the interference of absolute amplitude, so that the subsequent similarity calculation pays more attention to the relative relationship of frequency distribution, and makes the local frequency distortion of the normalized signal to be verified easier to quantify.

[0036] The signal strength analysis step S232 obtains the position of the target drone in the drone position list, determines the distance between the target drone and the signal receiving device, calculates the corresponding received power based on the distance using the free space path loss model, and determines whether the received power is consistent with the actual power of the signal to be verified. If not, it is a forged signal; wherein the received power is expressed as the following calculation model: , in, is the transmission power, and are the transmitting antenna gain and the receiving antenna gain, respectively. For distance, is the frequency of the normal signal, In the actual solution process, the transmitting antenna gain, receiving antenna gain, and normal signal frequency can be set accordingly according to whether the signal to be verified is an ADS-B signal or a Remote ID signal. For example, the ADS-B frequency is set to 1090MHz, the transmitting antenna gain is set to 2-3dBi, the Remote ID frequency is set to 2.4GHz or 5.8GHz, and the transmitting antenna gain is set to 5dBi.

[0037] Time-space consistency analysis step S233, continuously obtaining the location of the target drone and the signal to be verified , analyze whether the signal strength change during its movement conforms to the free space path loss model, whether the speed change is smooth, and analyze whether the signal frequency offset of the signal to be verified is consistent with expectations. If any of the analysis results is no, it is a fake signal.

[0038] Although low-cost equipment and ground equipment can forge signals, the forged signals may exhibit a spectrum distribution different from that of the real signals due to hardware limitations (such as transmission power or modulation accuracy). This embodiment is based on the above spectrum analysis step S231, and identifies abnormal signals forged by low-cost equipment by analyzing the frequency characteristics of the signal; considering that the forged signal generated by the ground equipment has a different transmission position from that of the aerial drone, which will cause a difference in signal strength, the signal strength analysis step S232 determines whether the signal strength of the signal to be verified is consistent with the distance to the target drone based on the radio signal propagation model, thereby troubleshooting the forged signal sent by the ground equipment.

[0039] In the above embodiment, the spatiotemporal consistency analysis step S233 further includes: Signal strength consistency analysis step S2331, according to the current position of the target drone, calculates the expected received power of the current position based on the free space path loss model, and determines the actual received power of the current position according to the signal to be verified, and calculates the mean square error between the actual received power and the expected received power. If it is greater than a set error value, the signal is judged to be abnormal. In some embodiments, the set error value is 5dB 2 Specifically, the current distance between the target drone and the signal receiving device at the current position is calculated, and the current distance is substituted into the calculation model of the received power to obtain the expected received power. , combined with the actual receiving power of the signal receiving device The mean square error (MSE) is calculated as: .

[0040] Position consistency analysis step S2332, calculating the moving speed and moving acceleration of the target drone according to its position, and if the moving acceleration is greater than an acceleration threshold, determining that the signal is abnormal, wherein the first acceleration threshold is configured as the maximum acceleration of the drone, and if the absolute value of the moving acceleration is greater than the acceleration threshold, determining that the position change of the target drone is not smooth, and there is a signal abnormality; Frequency consistency analysis step S2333, measure the signal frequency of the signal to be verified and calculate the absolute difference between the actual frequency offset and the expected frequency offset based on its position. If the absolute difference is greater than the offset threshold, the signal is judged to be abnormal. Optionally, the offset threshold is set to 100Hz. The signal frequency is expressed as , the expected frequency offset is calculated based on the following calculation model: ,in is the moving speed, is the angle between the target UAV motion direction vector and the sight line direction of the visual sensor. The sight line direction is the direction from the visual sensor of the inspection UAV A to the target UAV B. Figure 7 As shown; The actual frequency offset is calculated based on the following calculation model: .

[0041] Based on the above steps, the embodiment of the present application determines whether the signal conforms to the physical movement laws of the aircraft in the air based on the signal strength, position, and frequency changes, and performs multi-dimensional physical feature verification, thereby identifying the anomalies of the forged signal. It can identify the deception of the forged signal from the underlying signal characteristics, and efficiently identify the behavior of low-cost devices simulating aircraft. Multi-indicator parallel detection can be completed at the millisecond level, significantly improving the recognition efficiency. Signal strength calculation, position positioning and frequency change detection can all be processed in real time by embedded devices, meeting the real-time requirements of aircraft monitoring scenarios.

[0042] In some other embodiments, the illegal drone identification step S2 further includes: In the abnormal flight judgment step S24, for the drone identified as a real signal in the real signal judgment step S23, the heading information in the ADS-B signal is analyzed, and its position in the drone position list is judged to be compliant, that is, it is consistent with its preset route. If not, it is judged to be an abnormal flight and is an illegal drone.

[0043] Based on the above steps, the embodiments of the present application can promptly detect abnormal flight behaviors (such as deviation from the route, illegal intrusion into the no-fly zone) by real-time monitoring of the heading, and avoid potential safety accidents. UAV flights must comply with preset routes to ensure safe intervals from other aircraft and reasonable allocation of airspace resources. By parsing the heading information, it can be verified whether the drone is flying in compliance with regulations to avoid illegal operations. Moreover, there is no need for complex ground equipment, only airborne ADS-B equipment is required to broadcast and receive heading information, which is flexible to deploy.

[0044] Combination Figure 1As shown, the embodiment of the present application determines the illegal drones that do not send identity information within the visual range based on the signal presence judgment step S21, determines the illegal drones with unregistered identity information within the visual range based on the registration status judgment step S22, determines the illegal drones with forged identity information within the visual range based on the real signal judgment step S23, and determines the abnormal flying drones within the visual range based on the abnormal flight judgment step S24. The embodiment of the present application adopts the step-by-step screening method of steps S21~S23 to quantify, analyze and identify illegal flight behaviors from various dimensions, which not only improves the accuracy and efficiency of illegal flight monitoring and response, but also reduces safety risks and management costs.

[0045] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here. For example, multiple steps of steps S231 to S233 can be executed in parallel, and steps S2331 to S2333 can be executed in parallel.

[0046] In addition, combined Figures 2 to 6 The illegal drone identification method described in the embodiment of the present application can be implemented by a drone device used for aerial monitoring.

[0047] The monitoring drone device may include a processor and a memory storing computer program instructions.

[0048] Specifically, the processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0049] Among them, the memory may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a specific embodiment, the memory is a non-volatile memory. In a specific embodiment, the memory includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0050] The memory may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor.

[0051] The processor implements any one of the illegal drone identification methods in the above embodiments by reading and executing computer program instructions stored in the memory.

[0052] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0053] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for identifying illegal drones, characterized in that: include: A line-of-sight range detection step, detecting drones within the line-of-sight range through a visual sensor and locating the positions of the drones based on a preset target detection algorithm, and establishing a drone position list; The step of identifying illegal drones is to continuously receive ADS-B signals and Remote ID signals through a pre-installed signal receiving device, and based on the position of the drone and the ADS-B signal and Remote ID signal, verify in turn whether the corresponding drone within the line of sight sends ADS-B signals and Remote ID signals, whether the signal is a registered signal, and whether the registered signal is a real signal. If any of the verification results is no, it is judged to be an illegal drone.

2. The method for identifying illegal drones according to claim 1, characterized in that: The steps of identifying illegal drones include: A signal existence judgment step is to parse the ADS-B signal and Remote ID signal, obtain the location information in the ADS-B signal and / or Remote ID signal, compare it with the drone location list, identify the drone that does not send a signal as an illegal drone, and the drone that sends a signal enters the registration status judgment step for registration verification; The registration status judgment step is to parse the ICAO address in the ADS-B signal and / or the serial number in the Remote ID signal, perform real-time query verification in the drone supervision database, identify the drone sending the unregistered signal as an illegal drone, and the drone sending the registered signal enters the real signal judgment step for authenticity verification; The real signal judgment step performs frequency characteristic analysis, signal strength analysis and time-space consistency analysis on the ADS-B signal and Remote ID signal respectively, and judges whether the signal is a real signal or a forged signal by combining the verification results of the ADS-B signal and the Remote ID signal, and identifies the drone with forged signal as an illegal drone.

3. The method for identifying illegal drones according to claim 2, characterized in that: The real signal determination step further comprises: Spectrum analysis step, receiving the signal to be verified , perform fast Fourier transform on the signal to be verified to obtain the spectrum of the signal to be verified , and then normalize it to calculate the spectrum template of the normal signal The cosine similarity and Euclidean distance of the spectrum are combined to determine whether it is an abnormal spectrum, and if so, it is a forged signal; The signal strength analysis step is to obtain the position of the target drone in the drone position list, determine the distance between the target drone and the signal receiving device, calculate the corresponding received power based on the distance using a free space path loss model, and determine whether the received power is consistent with the actual power of the signal to be verified. If not, it is a forged signal; Spatiotemporal consistency analysis step, continuously obtaining the location of the target drone and the signal to be verified , analyze whether the signal strength change during its movement conforms to the free space path loss model, whether the speed change is smooth, and analyze whether the signal frequency offset of the signal to be verified is consistent with expectations. If any of the analysis results is no, it is a fake signal.

4. The method for identifying illegal drones according to any one of claims 1 to 3, characterized in that: The step of identifying illegal drones also includes: In the abnormal flight judgment step, for the drone identified as a real signal in the real signal judgment step, the heading information in its ADS-B signal is analyzed, and its position in the drone position list is judged to be compliant, that is, consistent with its preset route. If not, it is judged to be an illegal drone.

5. The method for identifying illegal drones according to claim 3, characterized in that: In the spectrum analysis step, the abnormal spectrum is a signal spectrum whose cosine similarity is lower than a first threshold and whose Euclidean distance exceeds a second threshold.

6. The method for identifying illegal drones according to claim 3, characterized in that: In the signal strength analysis step, the received power is expressed as the following calculation model: ， in, is the transmission power, and are the transmitting antenna gain and the receiving antenna gain, respectively. For distance, is the frequency of the normal signal, .

7. The method for identifying illegal drones according to claim 3, characterized in that: The spatiotemporal consistency analysis step further comprises: The signal strength consistency analysis step is to calculate the expected received power at the current position of the target UAV based on the free space path loss model, determine the actual received power at the current position based on the signal to be verified, and calculate the mean square error between the actual received power and the expected received power. If the error is greater than a set error value, the signal is judged to be abnormal. The position consistency analysis step is to calculate the moving speed and moving acceleration of the target UAV according to its position, and if the moving acceleration is greater than an acceleration threshold, it is determined that the signal is abnormal; The frequency consistency analysis step measures the signal frequency of the signal to be verified and calculates the absolute difference between the actual frequency offset and the expected frequency offset based on its position. If the absolute difference is greater than the offset threshold, the signal is judged to be abnormal.

8. The method for identifying illegal drones according to claim 7, characterized in that: In the signal strength consistency analysis step, the current distance between the target drone and the signal receiving device at the current position is calculated, and the current distance is substituted into the calculation model of the received power to obtain the expected received power. , combined with the actual receiving power of the signal receiving device The mean square error (MSE) is calculated as: 。 9. The method for identifying illegal drones according to claim 7, characterized in that: The first acceleration threshold is configured as the maximum acceleration of the drone. If the absolute value of the moving acceleration is greater than the acceleration threshold, it is determined that the position change of the target drone is not smooth and there is a signal abnormality.

10. The method for identifying illegal drones according to claim 9, characterized in that: In the frequency consistency analysis step, the signal frequency is expressed as , the expected frequency offset is calculated based on the following calculation model: ,in is the moving speed, is the angle between the target UAV's motion direction vector and the visual sensor's sight line direction, where the visual sensor points to the target UAV; The actual frequency offset is calculated based on the following calculation model: 。

Images