Unmanned aerial vehicle driving method based on micro-power interference optimization in urban environment

Abstract

The application discloses a kind of urban environment under the method for driving away unmanned aerial vehicle based on micro-power interference optimization, and the legality is determined by combining radio frequency fingerprint comparison white list, can effectively avoid the unmanned aerial vehicle of normal execution task to generate misdriving, to solve the problem of lack of distinguishing ability in prior art;Meanwhile, before interference, dynamically generate electromagnetic coexistence constraint set, protect public systems such as cellular communication, wireless local area network and emergency communication, and form null in the direction of base station, ensure that interference signal will not be leaked to affect urban public communication, overcome the deficiency that traditional high-power interference is easy to produce collateral damage to surrounding system;In the process of driving away execution, the present method determines frequency, power, time length and beam parameter by micro-power interference optimization, and uses phased array antenna directional transmission and the call of micro-power waveform library, realizes the targeted driving away of illegal unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle (UAV) repelling, and in particular to an unmanned aerial vehicle repelling method based on micro-power interference optimization in an urban environment. BACKGROUND

[0002] With the wide application of unmanned aerial vehicles (UAVs) in aerial photography, logistics transportation, security inspection, emergency rescue and other fields, the number of small unmanned aerial vehicles in urban airspace has rapidly increased. The flexibility and low cost of unmanned aerial vehicles provide new means for urban management and services. In a crowded city environment with complex communication systems, how to effectively and quickly repel illegal or unknown unmanned aerial vehicles has become a key technical problem to ensure public safety and social stability.

[0003] Currently, the mainstream anti-UAV technology mainly includes high-power electromagnetic interference, physical interception, net capture of unmanned aerial vehicles, and laser shooting down. Among them, high-power interference can force unmanned aerial vehicles to return or fail to a certain extent by shielding GPS / Beidou navigation signals or remote control links, but it often causes large-scale collateral damage to cellular communication, WiFi networks, and emergency communication systems in the city, posing a risk of "mistaken injury". Physical interception and net capture of unmanned aerial vehicles can directly stop the target, but they have high cost, complex execution, and the risk of secondary injury. Energy weapons such as lasers are difficult to deploy on a large scale due to urban building obstructions and safety concerns. In addition, some advanced unmanned aerial vehicles are equipped with anti-interference and autonomous navigation functions, and traditional high-power suppression methods have significantly reduced effectiveness, making it difficult to ensure the reliability and safety of repelling.

[0004] Therefore, in the repelling of unmanned aerial vehicles in an urban environment, there are problems such as the difficulty in controlling electromagnetic interference overflow effects, the rough repelling strategy leading to unmanned aerial vehicle out-of-control crashes, and the inability to balance public communication safety and repelling effectiveness. SUMMARY

[0005] The present application provides an unmanned aerial vehicle repelling method based on micro-power interference optimization in an urban environment, which aims to solve the problems of difficulty in controlling electromagnetic interference overflow effects, rough repelling strategy leading to unmanned aerial vehicle out-of-control crashes, and inability to balance public communication safety and repelling effectiveness in the repelling of unmanned aerial vehicles in an urban environment.

[0006] An unmanned aerial vehicle repelling method based on micro-power interference optimization in an urban environment, the method comprising:

[0007] Obtaining radio frequency, visual, and acoustic observation data, performing spectral analysis and direction estimation on the radio frequency signals, and combining the visual recognition and acoustic detection results to confirm the target unmanned aerial vehicle;

[0008] The radio frequency fingerprint of the target drone is compared with a white list database, if it is determined that it is an illegal drone, an electromagnetic coexistence constraint set is generated according to the occupation of cellular communication, wireless local area network and emergency communication, and a beam null is formed in the direction of the base station;

[0009] Micro-power interference optimization is performed under the constraint set to determine interference frequency points, transmission power, transmission time length and beam parameters;

[0010] At least one waveform is selected from a micro-power waveform library for directional transmission, the waveform library including symbol-level perturbation, subcarrier selective injection and navigation signal light disturbance waveform;

[0011] Interference is implemented on the target drone direction through a phased array antenna, and leakage data and target state are collected in real time by an external monitoring node, interference parameters are dynamically adjusted according to feedback results until the termination condition is met to end transmission.

[0012] In the above scheme, optionally, the acquisition of multi-modal observation data includes:

[0013] Wideband signal scanning is performed in the remote control and data transmission frequency bands commonly used by drones to identify suspicious signal types and strengths;

[0014] The shape characteristics of the target object are identified through an image acquisition device and the flight trajectory is continuously tracked;

[0015] The target noise is collected through an acoustic array and compared with a propeller noise feature library;

[0016] The above radio frequency detection, visual recognition and acoustic detection results are weighted and fused to output the target existence probability, spatial orientation and motion trend.

[0017] In the above scheme, optionally, the white list comparison includes:

[0018] The received radio frequency signal is standardized to extract frequency offset, modulation format, envelope shape and signal transient characteristics;

[0019] The extracted fingerprint of the legal drone in the white list database is compared for similarity, and the similarity is based on weighted matching of multi-dimensional features;

[0020] When the calculation result is lower than a preset threshold, the target drone is determined to be illegal, and a repulsion process is triggered.

[0021] In the above scheme, optionally, the generation of the electromagnetic coexistence constraint set includes:

[0022] Real-time analysis of base station scheduling information and shielding of cellular communication resource blocks in use;

[0023] Channel energy detection and beacon analysis are performed on the wireless local area network channel, and a channel in use is marked as a protected frequency point;

[0024] According to regulations, an emergency communication frequency range is preset, and the frequency range is added to a forbidden interference list;

[0025] Zero angle of the array antenna in the base station direction and the key facility direction is set to suppress interference radiation in the direction.

[0026] In the above scheme, optionally, the micro-power interference optimization comprises: selecting the nearest available interference frequency point according to the target unmanned aerial vehicle control link frequency range;

[0027] The initial transmission power is determined according to the flight height and distance of the target unmanned aerial vehicle, and is gradually fine-tuned during the interference process;

[0028] The transmission duration is set according to the speed and trajectory of the unmanned aerial vehicle, and is adjusted in real time in combination with the position update;

[0029] The beam pointing is dynamically adjusted according to the direction change of the unmanned aerial vehicle, and the beam width is narrowed or expanded according to the task requirement;

[0030] During the interference execution process, the frequency point selection, power level and beam parameters are iteratively corrected in combination with external monitoring feedback.

[0031] In the above scheme, optionally, the micro-power waveform library comprises:

[0032] Symbol-level perturbation waveform, for inserting a short-time narrowband pulse in the symbol time window of the remote control link to interfere with symbol judgment;

[0033] Subcarrier selective interference waveform, for injecting a low-power interference signal on the subcarrier corresponding to the unmanned aerial vehicle control channel;

[0034] Navigation signal light interference waveform, for applying a slight perturbation to the navigation signal frequency point to reduce the positioning reliability;

[0035] The waveforms can be called alone or combined according to a set order.

[0036] In the above scheme, optionally, the directional transmission comprises:

[0037] A phased array antenna with dynamic beam control is used to focus the interference signal on the target direction;

[0038] By adjusting the beam width, the energy is concentrated in the airspace where the unmanned aerial vehicle is located, and the sidelobe leakage is suppressed;

[0039] When there are multiple target unmanned aerial vehicles, beam switching or multi-beam mode is used to realize interference coverage in multiple directions.

[0040] Optionally, the working mode of the external monitoring node comprises:

[0041] The error vector magnitude and the adjacent channel leakage ratio of the transmission signal are collected in real time by the external monitoring node arranged at the periphery of the protection area.

[0042] The monitoring data are transmitted to the interference optimization module through the feedback link.

[0043] When it is detected that the leakage level exceeds the preset threshold, the interference optimization module is driven to reduce the transmission power or adjust the beam direction to ensure that the interference is within the safe range.

[0044] Optionally, the termination condition comprises one of the following conditions:

[0045] It is detected that the target UAV has left the boundary of the protection area;

[0046] It is detected that the target UAV has triggered the automatic return mode;

[0047] It is detected that the target UAV has landed or the signal has disappeared;

[0048] It is detected that the interference leakage fed back by the external monitoring node has decreased below the safe threshold.

[0049] Optionally, the method further comprises a data recording and tracing step, which comprises:

[0050] The interference parameter configuration, the transmission frequency point selection, the power level, the beam pointing and the transmission time length are continuously recorded during the expulsion process;

[0051] The leakage data fed back by the external monitoring node and the state change of the target UAV are recorded at the same time;

[0052] After the expulsion process is completed, the above records are stored in a log file or a database, and a corresponding time stamp and a unique identifier are generated for subsequent event review and tracing.

[0053] Compared with the prior art, the present application has at least the following beneficial effects:

[0054] The present application is based on further analysis and research on the problems of the prior art. It is found that in the process of driving away the unmanned aerial vehicle in the urban environment, it is difficult to control the electromagnetic interference overflow effect, the driving strategy is too rough, leading to the unmanned aerial vehicle out of control and falling, and the conflict between public communication safety and driving effectiveness cannot be considered. Through multi-modal detection means, the unmanned aerial vehicle target can be accurately identified, and the legality can be determined by combining the radio frequency fingerprint comparison whitelist, which can effectively avoid the misdriving of the unmanned aerial vehicle normally performing the task, thereby solving the problem of lack of distinguishing ability in the prior art. At the same time, the electromagnetic coexistence constraint set is dynamically generated before the interference, and the public systems such as cellular communication, wireless local area network and emergency communication are protected, and a null is formed in the direction of the base station to ensure that the interference signal will not be leaked to affect the urban public communication, overcoming the deficiency that the traditional high-power interference is easy to cause incidental damage to the surrounding systems. In the process of driving away, the frequency, power, time and beam parameters are determined by the micro-power interference optimization, and the directional transmission of the phased array antenna and the calling of the micro-power waveform library are used to realize the targeted driving away of the illegal unmanned aerial vehicle, avoiding the problems of high energy consumption and uncontrollability caused by extensive full-frequency interference. In addition, the introduction of the external monitoring node forms a closed loop in the driving away process, which can automatically adjust the parameters according to the real-time feedback and stop transmitting when the termination condition is met, so as to ensure the safety, controllability and convergence of the whole process. Therefore, the present application can solve the problems of insufficient identification accuracy, large interference side effects and lack of fine control in the prior art in the complex urban environment, and realize safe, low-power and controllable driving away of the unmanned aerial vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The flowchart of the method for driving away the unmanned aerial vehicle based on micro-power interference optimization in the urban environment provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0057] In the description of the present application: unless otherwise specified, the meaning of "multiple" is two or more. The terms "first", "second", "third" and the like in the present application are intended to distinguish the objects referred to, and do not have special meaning in the technical aspect (for example, it should not be understood as emphasizing importance or order, etc.). The expressions "include", "contain", "have" and the like also mean "not limited to" (some units, components, materials, steps, etc.).

[0058] In one embodiment, as shown in Figure 1As shown, a method for driving away a UAV based on micro-power interference optimization in an urban environment is provided, comprising the following steps:

[0059] Obtaining radio frequency, visual and acoustic observation data, performing spectrum analysis and direction estimation on the radio frequency signal, and combining visual recognition and acoustic detection results to confirm the target UAV;

[0060] Comparing the radio frequency fingerprint of the target UAV with the white list database, if it is determined to be an illegal UAV, generating an electromagnetic coexistence constraint set according to the occupation situation of cellular communication, wireless local area network and emergency communication, and forming a beam null in the direction of the base station;

[0061] Performing micro-power interference optimization under the constraint set to determine the interference frequency point, transmission power, transmission time length and beam parameter;

[0062] Selecting at least one waveform from a micro-power waveform library for directional transmission, the waveform library including symbol-level perturbation, subcarrier selective injection and navigation signal light disturbance waveform;

[0063] Implementing interference on the target UAV direction through a phased array antenna, and collecting leakage data and target state in real time by an external monitoring node, dynamically adjusting the interference parameters according to the feedback results, and ending the transmission after the termination condition is met.

[0064] In this embodiment, a method for driving away a UAV based on micro-power interference optimization in an urban environment is provided. This method addresses the problems of existing technologies, such as high-power interference causing collateral damage, extensive and uncontrollable driving methods. It proposes a complete process of multi-modal detection and recognition, legality determination, electromagnetic environment constraint, micro-power interference optimization, waveform library calling, directional transmission and closed-loop monitoring control, to achieve safe driving away of illegal UAVs.

[0065] First, in the multi-modal observation link, this method deploys radio frequency receiving equipment, visual camera equipment and acoustic array to achieve all-around monitoring of the airspace in the protected area. The radio frequency part uses a wideband receiver to scan the remote control link and data transmission link commonly used by UAVs, and preliminarily identifies the energy distribution and modulation characteristics of the signal to obtain the wireless link characteristics of potential UAVs. The visual part collects airspace images through optical cameras and infrared devices, and identifies aircraft shape features combined with target detection algorithms, further predicting and analyzing the motion trajectory. The acoustic part collects environmental audio through array microphones, compares with the propeller noise library, and judges whether there are acoustic characteristics unique to UAVs. By fusing radio frequency, visual and acoustic information, this method can accurately identify UAV targets in a complex urban background, avoiding false positives due to the limitations of single sensors.

[0066] In the legality judgment link, the method further introduces a radio frequency fingerprint recognition mechanism. By extracting features from the identified drone signals, including frequency offset, modulation transient response, envelope shape, etc., the method compares them with the whitelist database. When the similarity between the target features and the samples recorded in the legal drone database is lower than the threshold, the target is determined to be an illegal drone, and the repelling process is started. In this way, the method can effectively distinguish between drones performing normal tasks and potential threat drones, ensuring the relevance and legality of the repelling behavior.

[0067] In the electromagnetic environment constraint generation link, the method fully considers the complexity of urban environment and public communication needs. Specifically, the system analyzes the scheduling information of cellular communication base stations in real time, marks the physical resource blocks in use as protected frequency bands; through energy detection and beacon analysis of wireless local area network channels, it identifies occupied channels and adds them to the protection list; at the same time, according to regulations, it presets emergency communication frequency bands, including public security, fire, medical special networks, etc., which are used as hard constraints and are not allowed to be interfered. In the spatial domain, the system forms nulls in the direction of base stations or other sensitive facilities through phased array antennas to ensure that the interference signal does not leak to the key direction. The above measures together constitute the electromagnetic coexistence constraint set, providing basic conditions for subsequent interference optimization.

[0068] In the micro-power interference optimization link, the method does not use traditional high-power full-frequency interference, but selects the optimal frequency point outside the constraint set and sets the minimum feasible transmission power. It also dynamically adjusts the transmission duration according to the target drone's flight height and speed. At the same time, it corrects the beam direction and beam width according to the target position and trajectory to achieve accurate tracking of the drone. Through dynamic optimization strategies, the system can minimize the impact on the surrounding electromagnetic environment while ensuring the effectiveness of the repelling, achieving the design concept of "minimum necessary interference".

[0069] In the waveform selection link, the method establishes a micro-power waveform library, including symbol-level perturbation waveforms, subcarrier selective injection waveforms, and navigation signal light disturbance waveforms. Symbol-level perturbation waveforms are used to insert short interference pulses within the symbol time window of the drone remote control link, reducing the accuracy of symbol decision; subcarrier selective injection waveforms only apply interference signals to the subcarriers corresponding to the control channel, avoiding excessive interference to the data transmission; navigation signal light disturbance waveforms apply slight disturbance to the satellite navigation frequency point, causing the navigation solution process of the drone to decrease in reliability, triggering its return mode. The method can select a single waveform or use a combination according to the drone model, flight state, and environmental conditions, forming a flexible interference strategy.

[0070] In the directional transmission and closed-loop control link, the method uses a phased array antenna to directionally transmit to the target UAV. The phased array antenna can adjust the beam direction and width according to the real-time position of the UAV, so that the interference energy is concentrated on the target UAV, and the sidelobe energy is effectively controlled. At the same time, the monitoring nodes deployed outside the protection area continuously collect indicators such as error vector amplitude and adjacent channel leakage ratio, and feed the data back to the interference optimization module. When the monitoring node finds that the interference leakage exceeds the threshold, the system automatically reduces the transmission power or corrects the beam direction to ensure that the public communication system is not affected. The closed-loop mechanism ensures the safety and controllability of the system during the driving process.

[0071] In the termination link, the method sets multiple exit conditions, including the target UAV leaving the protection area boundary, the target UAV triggering the automatic return mode, the target UAV landing or signal disappearing, and the interference leakage feedback by the external monitoring node recovering to the safety threshold. When any condition is met, the system immediately terminates transmission to avoid unnecessary continuous interference, and records the parameter configuration, monitoring data and UAV state during the interference process for subsequent review and tracing.

[0072] The method can achieve safe driving of illegal UAVs in complex urban environments. The technical effects mainly include the following aspects: 1. High-precision identification and positioning of UAV targets are achieved; 2. Directional driving is implemented without interfering with cellular communication, wireless local area network and emergency communication; 3. Low-power optimization and waveform library calling are realized to achieve low-power and targeted interference; 4. Through the closed-loop monitoring and feedback mechanism, the interference process is controllable and safe; 5. Through the termination condition and data recording mechanism, the entire driving process meets the regulatory requirements and is traceable. Thus, the method effectively overcomes the defects of high-power and extensive interference in the prior art, and realizes efficient, safe and controllable UAV driving in urban environments.

[0073] In this embodiment, the acquisition of multi-modal observation data includes:

[0074] Wideband signal scanning is performed in the remote control and data transmission frequency bands commonly used by UAVs to identify suspicious signal types and strengths;

[0075] The image acquisition device identifies the shape characteristics of the target object and continuously tracks the flight trajectory;

[0076] The acoustic array acquires the target noise and compares it with the propeller noise feature library;

[0077] The above radio frequency detection, visual recognition and acoustic detection results are weighted and fused to output the target existence probability, spatial orientation and motion trend.

[0078] The embodiment provides a detailed implementation, through effective integration of different sensor outputs, the detection and identification precision of the unmanned aerial vehicle target in the complex urban environment can be greatly improved, and performance decline of a single sensor under shielding, noise interference or environmental complexity is avoided. The specific implementation and technical effects of each step are described in detail below.

[0079] First, in the radio frequency observation link, the embodiment arranges a wideband radio frequency receiving device in the target airspace, which is used for scanning the frequency band commonly used by the unmanned aerial vehicle. Specifically, the radio frequency receiving device can cover the civil remote control link frequency bands such as 2.4 GHz and 5.8 GHz, and simultaneously support extended monitoring of navigation frequency points and part of the special data transmission frequency bands. In actual operation, the receiving device performs wideband sampling on the airspace signal, and performs energy detection and signal classification on the sampling result. The energy detection can help to judge whether there is a signal activity with significant intensity on certain frequency points, and the signal classification preliminarily identifies the suspected unmanned aerial vehicle remote control link or data transmission link by comparing the modulation mode, bandwidth characteristics and burst mode. Through this process, the system can quickly lock the possible unmanned aerial vehicle communication signal in the frequency spectrum, laying a foundation for subsequent processing.

[0080] Further, in the visual recognition link, the embodiment arranges high-definition cameras, panoramic cameras and infrared monitoring devices in the protection area to capture and identify the flying objects in the airspace. The visual detection algorithm based on the convolutional neural network can automatically identify the shape characteristics of the unmanned aerial vehicle, such as the body shape, the number of rotors, the flight attitude and the like. At the same time, the system can track the identified unmanned aerial vehicle, and analyze the change trend of the flight trajectory. Through the image information of the continuous frames, the flight direction and speed of the unmanned aerial vehicle can be predicted, which provides a basis for subsequent interference strategy adjustment. In the night or low-visibility conditions, the infrared device can supplement the deficiency of the optical camera, and ensure the all-weather visual coverage.

[0081] In the acoustic detection link, the embodiment uses the array microphone arranged around the protection area to collect audio signals of the airspace environment in real time. The unmanned aerial vehicle produces a unique propeller noise in the flight process, and the spectral distribution of the noise is significantly different from natural noise, wind noise or traffic noise. The system compares the collected audio signals with the pre-constructed propeller noise feature library, and judges whether the target is an unmanned aerial vehicle through parameters such as spectral peak, bandwidth and modulation texture. Since the acoustic signal has the characteristic of omnidirectional propagation, in the case that the vision is blocked or the radio frequency signal is weakened, the acoustic detection can still provide effective auxiliary information.

[0082] In the multi-modal data fusion link, the embodiment adopts a weighted fusion manner to integrate the results of radio frequency detection, visual recognition and acoustic detection. Specifically, the system assigns a credibility weight to each of the three types of detection results. For example, in a daytime environment without occlusion, the weight of visual recognition is higher; in a nighttime or insufficiently illuminated environment, the weight of acoustic detection is increased; and in an environment with strong electromagnetic interference, the weight of radio frequency detection is increased. Through this dynamic weight mechanism, the system can flexibly adjust the basis for decision-making according to the actual environmental conditions, thereby improving the accuracy of unmanned aerial vehicle recognition. Finally, the fusion module outputs the probability value, spatial orientation and motion trend information of the target existence.

[0083] In the specific implementation process, the embodiment also performs confidence assessment on the results of data fusion. When the three types of detection results are highly consistent, the target existence probability output by the system is close to 100%, indicating that the recognition result is reliable; when there are differences among the three types of results, the system outputs an intermediate probability through weighted calculation and triggers a further verification mechanism. For example, if radio frequency detection and acoustic detection both indicate the existence of a target, but visual detection does not find the target, the system will continue to track the radio frequency signal and expand the visual search range to avoid missing the target due to the absence of a single detection module.

[0084] The multi-modal observation data acquisition and fusion described in the embodiment can effectively overcome the limitations of a single sensor and achieve accurate recognition of unmanned aerial vehicle targets in complex urban environments. The technical effects mainly include:

[0085] Improved recognition accuracy: The three types of detection results (radio frequency, vision and acoustic) confirm each other, reducing false positives and false negatives.

[0086] Enhanced environmental adaptability: The system can automatically adjust the detection weights according to daytime, nighttime, unoccluded or occluded, low-noise or high-noise environments.

[0087] Real-time performance is guaranteed: Multiple modal sensors work simultaneously, reducing the delay caused by processing a single module and improving overall response speed.

[0088] Robustness is improved: In extreme environments such as radio frequency interference or insufficient light, acoustic or other modalities can still maintain detection capabilities.

[0089] The technical solution described in the embodiment achieves high-precision detection and recognition of unmanned aerial vehicle targets in urban environments, providing reliable basic information for subsequent legality determination and interference execution.

[0090] In the embodiment, the white list comparison includes:

[0091] Standardizing the received radio frequency signals to extract frequency offset, modulation format, envelope shape and signal transient characteristics;

[0092] The extracted fingerprint is compared with legal drone fingerprints in a whitelist database, and the similarity is based on weighted matching of multi-dimensional features;

[0093] When the calculation result is lower than a preset threshold, the target drone is determined to be illegal, and a repelling process is triggered.

[0094] The embodiment proposes a legal drone fingerprint comparison-based illegal drone determination method. This method mainly solves the problem that legal drones and illegal drones are difficult to distinguish in the prior art, ensuring that the repelling measure only acts on illegal targets and does not interfere with normal flying drones. The specific implementation of each step of the method and the corresponding technical effects will be described in detail below.

[0095] First, in the radio frequency signal collection stage, the embodiment collects drone signals in the airspace in real time through a wideband receiver deployed in the protected area. When a drone performs a flight task, its remote control link or data transmission link will continuously emit radio frequency signals with obvious characteristics. By sampling and demodulating the received signals, the original data of the frequency offset, modulation format, transient response, and envelope shape can be obtained. These data are the basis for subsequent radio frequency feature extraction.

[0096] In the feature extraction stage, the system performs standardization processing on the collected radio frequency signals. The standardization process includes signal amplitude normalization, frequency offset correction, noise component filtering, and other operations to ensure the accuracy of subsequent feature extraction. In the standardized signal, the system focuses on extracting the following features: first, the frequency offset, which is the slight deviation of the transmitter from the carrier frequency, which is an inherent characteristic of the drone hardware device; second, the modulation format feature, including the modulation order, symbol rate, and envelope shape; third, the transient response feature, which is the short-time feature of the signal when starting or switching; and fourth, the overall envelope feature, which describes the pattern of signal amplitude change over time. These features together constitute the radio frequency fingerprint of the drone.

[0097] In the database construction stage, the embodiment establishes a legal drone radio frequency fingerprint whitelist database. This database samples and records each authorized drone to form a stable radio frequency feature file, and calibrates the feature range of legal drones of different models and batches. The database can be indexed by model, serial number, etc. to support fast retrieval. During system operation, the database can be maintained through a remote update mechanism to ensure that newly added legal drones can be timely recorded.

[0098] In the comparison and determination stage, the embodiment adopts a similarity calculation method to compare the real-time extracted target UAV radio frequency fingerprint with the recorded fingerprint in the database. Specifically, the system calculates the similarity score through multi-dimensional feature weighted matching, and the weight of different feature dimensions can be set according to the actual application scene. For example, in the urban environment, the frequency offset feature has high stability and can be given a higher weight; while in the environment with strong signal interference, the weight of the transient response feature can be increased. When the similarity score is higher than the threshold, the system determines that the target is a legal UAV; when the similarity score is lower than the threshold, it is determined that the target is an illegal UAV, and the driving process is triggered.

[0099] In actual application, the embodiment also considers edge cases. If the characteristics of the target UAV are highly similar to but not completely matched with the legal UAV, the system will give a "suspicious" determination and further verify it in combination with the visual and acoustic detection results. For example, if the visual module confirms that the UAV shape is not in the legal model library, and the radio frequency comparison result is lower than the threshold, the target is determined to be an illegal UAV, thereby improving the reliability of the determination.

[0100] In terms of specific implementation, the embodiment can improve the comparison efficiency through hardware acceleration. The extraction of radio frequency characteristics and similarity calculation can be accelerated through field programmable gate array (FPGA) or graphics processing unit (GPU) to meet the real-time requirements. At the same time, the database retrieval can also use hierarchical indexing or hash matching method to improve the comparison speed under a large number of legal UAV samples.

[0101] Through the above steps, the white list comparison mechanism can quickly and accurately distinguish legal UAVs from illegal UAVs, avoiding the false injury problem caused by the "one-size-fits-all" driving method in the prior art.

[0102] The technical effects mainly reflect in the following aspects:

[0103] First, the legality determination is accurate. Since the radio frequency fingerprint is difficult to forge and has device uniqueness, the determination result has high credibility.

[0104] Second, the driving process is targeted. The system starts the interference process only after confirming that the target is an illegal UAV, ensuring that the normal flight activities in the public airspace are not affected.

[0105] Third, the database is scalable. The white list database of the embodiment supports dynamic updating and can adapt to the increasing number of legal UAV models and batches, ensuring long-term effectiveness.

[0106] Fourth, the determination mechanism is robust. Through weighted comparison and multi-modal auxiliary verification, high recognition rate can be maintained in complex electromagnetic environments, and the false rejection rate can be reduced.

[0107] The technical solution defined in the embodiment effectively solves the problem of unclear distinction between legal and illegal targets in the process of driving away the unmanned aerial vehicle, and can provide an accurate and reliable illegal unmanned aerial vehicle identification method in a complex urban environment, thereby providing a solid foundation for the implementation of subsequent driving measures.

[0108] In the embodiment, the generation of the electromagnetic coexistence constraint set comprises:

[0109] Real-time analysis of base station scheduling information and shielding of the cellular communication resource block being used;

[0110] Channel energy detection and beacon analysis are performed on the wireless local area network channel, and the channel being used is marked as a protected frequency point;

[0111] According to the regulations, the emergency communication frequency range is preset, and the frequency range is added to the prohibited interference list;

[0112] The null angle of the array antenna is set in the direction of the base station and the key facility to suppress the interference radiation in that direction.

[0113] The embodiment provides a generation mode of an electromagnetic coexistence constraint set. The mode aims to solve the problem that in the prior art, the interference signal may cause collateral damage to cellular communication, wireless local area network, and emergency communication systems during the process of driving away the unmanned aerial vehicle. By generating a multi-dimensional constraint set before interference implementation, it can be ensured that the driving operation is performed without damaging the urban public communication order, thereby meeting the safety and reliability requirements in actual applications. The specific implementation mode and technical effects of the technical solution are described in detail below.

[0114] In terms of cellular communication protection, the embodiment interfaces with the urban communication base station system to obtain the physical resource block scheduling information of the base station in real time. In the cellular communication network, the physical resource block is the basic unit of scheduling, and different time and frequency resources are allocated to different users. When the system detects that the base station is scheduling certain frequency points, these frequency points are automatically marked as protected resources, and interference is prohibited within these frequency point ranges. This mechanism can ensure that the driving measures do not interfere with cellular communication, avoiding user disconnection or communication quality degradation.

[0115] In terms of wireless local area network protection, the embodiment uses a combination of channel energy detection and beacon frame analysis to dynamically obtain the channel occupation status of the current Wi-Fi network. Energy detection can be used to determine the current energy level of each channel, and beacon frame analysis can identify the access point information running on the channel. By combining the results of the two, the system can accurately identify the occupied channels and add them to the protection list, thereby ensuring that the interference signal does not cover the frequency range being used by Wi-Fi, and protecting the stability of data communication in the local area network.

[0116] In terms of emergency communication protection, the embodiment presets communication frequency bands dedicated to emergency departments such as public security, fire control and medical treatment according to relevant regulations, and takes these frequency bands as hard constraints that cannot be touched. In the process of driving, no matter how the optimization model adjusts, these frequency bands must be avoided to ensure the stability and reliability of the communication link of the emergency system. In this way, even in large-scale driving operations, there will be no impact on key public safety communications.

[0117] In addition to the protection in the frequency dimension, the embodiment also sets constraints in the spatial domain. Through the beamforming function of the phased array antenna, the system can form nulls in a specific direction. A null is to suppress the transmitted energy to a very low level in some sensitive directions, so that the intensity of the interference signal in that direction tends to zero. In urban environments, this technology is particularly important, for example, nulls can be set in the direction of base stations, communication towers, television broadcast stations or other sensitive facilities to avoid leakage of interference beam energy to these key devices, thereby ensuring their normal operation.

[0118] In the process of generating the constraint set, the embodiment integrates four types of information: cellular communication protection, wireless local area network protection, emergency communication protection and spatial null protection, to form a complete multi-dimensional constraint set. This set covers the frequency, time and space dimensions, providing input conditions for subsequent micro-power interference optimization. After receiving the constraint set, the optimization module can only perform parameter selection within the remaining available frequency and spatial range, thereby achieving the goal of "minimum interference within the allowed range".

[0119] In terms of specific implementation, the embodiment realizes the automatic generation and calling of the constraint set through the software-defined radio platform and the phased array control platform. The radio frequency receiving module is responsible for real-time sensing of spectrum occupation, the beacon analysis module is responsible for identifying Wi-Fi channels, the database module stores emergency frequency band information, and the antenna control module is responsible for forming nulls in the specified direction. The outputs of all modules are unified into the interference optimization module to ensure that the final interference parameter selection does not violate the constraint conditions. The electromagnetic coexistence constraint set generation method can fully investigate and avoid potential interference risks before the driving process begins.

[0120] The technical effects mainly embodied in the following aspects: first, effective protection of cellular communication is realized to avoid public mobile communication service interruption caused by the exclusion measure; second, the normal operation of the wireless local area network is ensured to avoid interference to daily data communication of residents and enterprises; third, the absolute safety of emergency communication such as public security, fire fighting and medical treatment is ensured to make the exclusion measure not hinder rescue and emergency command when the emergency occurs; fourth, through the spatial null technology, the interference beam energy leakage to the sensitive device direction is effectively prevented, and the influence on the urban information infrastructure is reduced; fifth, various constraint information is unified and integrated to form a systematic electromagnetic coexistence protection mechanism, so that the interference optimization module has a clear boundary when performing parameter selection, thereby realizing compliant and safe exclusion operation.

[0121] Through the multi-dimensional constraint set generation, zero influence on public communication and key facilities in the process of drone exclusion is ensured, and the dual goals of "interference and coexistence" in the urban environment are truly realized, and the prominent problem of communication system damage caused by high-power interference in the prior art is solved.

[0122] In the embodiment, the micro-power interference optimization includes: selecting the nearest available interference frequency point according to the target drone control link frequency band;

[0123] The initial transmission power is determined according to the flight height and distance of the target drone, and is gradually fine-tuned during the interference process;

[0124] The transmission duration is set according to the speed and trajectory of the drone, and is adjusted in real time in combination with the position update;

[0125] The beam pointing is dynamically adjusted according to the direction change of the drone, and the beam width is narrowed or expanded according to the task demand;

[0126] During the interference execution process, the frequency point selection, power level and beam parameters are iteratively corrected in combination with external monitoring feedback.

[0127] In the embodiment, the micro-power waveform library includes:

[0128] Symbol-level perturbation waveform, used for inserting a short-time narrowband pulse in the symbol time window of the remote control link to interfere with symbol judgment;

[0129] Subcarrier selective interference waveform, used for injecting a low-power interference signal on the subcarrier corresponding to the drone control channel;

[0130] Navigation signal light interference waveform, used for applying a slight perturbation at the navigation signal frequency point to reduce the positioning reliability;

[0131] The waveforms can be called alone or combined according to the set order.

[0132] In the embodiment, the directional transmission comprises:

[0133] The phased array antenna with dynamic beam control is used to focus the interference signal on the target direction;

[0134] The energy is concentrated in the airspace where the UAV is located by adjusting the beam width, and the sidelobe leakage is suppressed;

[0135] When there are multiple target UAVs, beam switching or multi-beam mode is used to realize interference coverage in multiple directions.

[0136] In the embodiment, the working mode of the external monitoring node comprises:

[0137] It is arranged on the periphery of the protection area to collect the error vector magnitude and adjacent channel leakage ratio of the transmission signal in real time;

[0138] The monitoring data is transmitted to the interference optimization module through the feedback link;

[0139] When it is detected that the leakage level exceeds the preset threshold, the interference optimization module is driven to reduce the transmission power or adjust the beam direction to ensure that the interference is within the safety range.

[0140] In the embodiment, the termination condition comprises one of the following conditions:

[0141] It is detected that the target UAV has left the boundary of the protection area;

[0142] It is detected that the target UAV has triggered the automatic return mode;

[0143] It is detected that the target UAV has landed or the signal disappears;

[0144] It is detected that the interference leakage feedback by the external monitoring node has decreased below the safety threshold.

[0145] In the embodiment, the method further comprises a data recording and tracing step, which comprises:

[0146] The interference parameter configuration, transmission frequency point selection, power level, beam pointing and transmission time during the driving process are continuously recorded;

[0147] The leakage data feedback by the external monitoring node and the state change of the target UAV are recorded at the same time;

[0148] After the driving process is completed, the above records are stored in a log file or a database, and a corresponding timestamp and unique identifier are generated for subsequent event review and tracing.

[0149] The method realizes accurate identification of the unmanned aerial vehicle target through multi-modal detection means, and combines the radio frequency fingerprint comparison whitelist to complete the legality determination, which can effectively avoid the misdriving of the unmanned aerial vehicle normally performing the task, thereby solving the problem of lack of distinguishing ability in the prior art; at the same time, the electromagnetic coexistence constraint set is dynamically generated before the interference, the public systems such as cellular communication, wireless local area network and emergency communication are protected, and a null is formed in the direction of the base station, so that the interference signal cannot be leaked to affect the urban public communication, and the deficiency of the traditional high-power interference being easy to cause incidental damage to the surrounding systems is overcome; during the driving-off execution process, the method optimizes the frequency, power, time length and beam parameters through the micro-power interference, and utilizes the phased array antenna directional transmission and the calling of the micro-power waveform library, so that the targeted driving-off of the illegal unmanned aerial vehicle is realized, and the problems of high energy consumption and uncontrollability caused by extensive full-frequency interference are avoided; in addition, the introduction of the external monitoring node forms a closed loop for the driving-off process, which can automatically adjust the parameters according to the real-time feedback and stop transmitting when the termination condition is met, so as to ensure the safety, controllability and convergence of the whole process. Therefore, the present application can solve the problems of insufficient identification accuracy, large interference side effects and lack of fine control in the prior art in the complex urban environment, and realize safe, low-power and controllable unmanned aerial vehicle driving-off.

[0150] The scheme of the present application is limited to the implementation by the public management department (authorized by the government or endowed with power by law) or the designated unit thereof, and the specific implementation process should comply with relevant laws and regulations.

[0151] The technical features of the above embodiments can be combined arbitrarily, and in order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

Claims

1. A method for driving away unmanned aerial vehicles (UAVs) in an urban environment based on low-power interference optimization, characterized in that, The method includes: Acquire radio frequency, visual and acoustic observation data, perform spectrum analysis and direction estimation on radio frequency signals, and combine visual recognition and acoustic detection results to confirm the target UAV; The radio frequency fingerprint of the target drone is compared with the whitelist database. If it is determined to be an illegal drone, an electromagnetic coexistence constraint set is generated based on the occupancy of cellular communication, wireless local area network and emergency communication, and a beam null is formed in the direction of the base station. Under the set of constraints, perform low-power interference optimization to determine the interference frequency, transmission power, transmission duration, and beam parameters; Select at least one waveform from a low-power waveform library for directional transmission. The waveform library includes symbol-level perturbation, subcarrier selective injection, and navigation signal light perturbation waveforms. The phased array antenna interferes with the direction of the target UAV, and the external monitoring node collects the leaked data and target status in real time. The interference parameters are dynamically adjusted according to the feedback results until the termination conditions are met and the launch ends. The generation of the electromagnetic coexistence constraint set includes: Real-time analysis of base station scheduling information and blocking of cellular communication resource blocks in use; Channel energy detection and beacon resolution are performed on wireless LAN channels, and the channels in use are marked as guard frequencies; In accordance with regulations, the emergency communication frequency band range is pre-defined and added to the prohibited interference list; Set the null angle of the array antenna in the direction of the base station and the direction of key facilities to suppress interference radiation in the direction of key facilities; The external monitoring node operates in the following ways: Deployed outside the protected area to collect the error vector amplitude and adjacent channel leakage ratio of the transmitted signal in real time; The monitoring data is transmitted to the interference optimization module via a feedback link; When the detected leakage level exceeds a preset threshold, the interference optimization module is driven to reduce the transmission power or adjust the beam direction to ensure that the interference is within a safe range.

2. The method according to claim 1, characterized in that, Acquiring multimodal observation data includes: Broadband signal scanning is performed in the commonly used remote control and data transmission frequency bands of UAVs to identify suspicious signal types and strengths; The system uses image acquisition equipment to identify the shape features of target objects and continuously track their flight trajectories. Target noise is collected using an acoustic array and compared with a propeller noise feature library; The results of radio frequency detection, visual recognition and acoustic detection are weighted and fused to output the probability of target existence, spatial orientation and motion trend.

3. The method according to claim 1, characterized in that, This also includes whitelist comparison: The received radio frequency signal is standardized to extract frequency offset, modulation format, envelope shape and transient signal characteristics; The extracted fingerprints are compared with the fingerprints of legitimate drones in the whitelist database. The similarity is calculated based on a weighted matching of multi-dimensional features. When the calculation result is lower than the preset threshold, the target drone is determined to be illegal, and the expulsion process is triggered.

4. The method according to claim 1, characterized in that, The low-power interference optimization includes: selecting the nearest available interference frequency point based on the target UAV control link frequency band; The initial transmission power is determined based on the target UAV's flight altitude and distance, and is gradually fine-tuned during the jamming process; The launch duration is set based on the drone's speed and trajectory, and adjusted in real time in conjunction with location updates; The beam direction is dynamically adjusted according to changes in the UAV's orientation, and the beam width is narrowed or expanded according to mission requirements. During the interference process, external monitoring feedback is used to iteratively correct the frequency selection, power level, and beam parameters.

5. The method according to claim 1, characterized in that, The low-power waveform library includes: Symbol-level perturbation waveforms are used to insert short-duration narrowband pulses within the symbol time window of the remote control link to interfere with symbol decision; Subcarrier selective interference waveform, used to inject low-power interference signals on the subcarriers corresponding to the UAV control channel; Navigation signal perturbation waveform, used to apply slight perturbations to the navigation signal frequency to reduce positioning reliability; Symbol-level disturbance waveforms, subcarrier-selective interference waveforms, and navigation signal minor disturbance waveforms can be called individually or in combination according to a set order.

6. The method according to claim 1, characterized in that, The directed launch includes: A phased array antenna with dynamic beam control is used to focus the interference signal toward the target direction; By adjusting the beamwidth, energy is concentrated in the airspace where the UAV is located, and sidelobe leakage is suppressed; When multiple target drones are present, interference coverage in multiple directions can be achieved through beam switching or multi-beam mode.

7. The method according to claim 1, characterized in that, The termination condition includes one of the following: The target drone has been detected to have left the protected area boundary. The target drone has been detected to have triggered automatic return-to-home mode; The target drone has been detected as having landed or lost signal. The interference leakage detected by the external monitoring node has been reduced to below the safety threshold.

8. The method according to claim 1, characterized in that, The method further includes a data recording and traceability step, which includes: During the expulsion process, the interference parameters, transmission point selection, power level, beam direction, and transmission duration were continuously recorded. Simultaneously record the leaked data and changes in the target drone's status reported by external monitoring nodes; After the expulsion process is completed, the records will be stored in a log file or database, and a corresponding timestamp and unique identifier will be generated for subsequent event review and traceability.

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