A method for detecting and countering unmanned aerial vehicles (UAVs)

Abstract

This invention relates to a method for detecting and countering unmanned aerial vehicles (UAVs). It deploys a multi-dimensional sensing network for instantaneous scanning across the entire frequency band, simultaneously acquiring noise characteristics of radio signals, material characteristics in the terahertz band, and Doppler effect characteristics. These are mapped to a virtual twin to reconstruct the physical parameters, communication protocols, and behavioral patterns of the target UAV. A deep comparison is then performed using a camouflage sample library generated by an adversarial generative network to generate a holographic target profile containing three-dimensional motion vectors, energy characteristics, and potential threat intent. Quantum entanglement signals are used to interfere with the target's quantum-encrypted communication link, generating a controllable plasma cloud to selectively attenuate its navigation and image transmission signals. The controllability of the target is assessed, and uncontrollable targets are driven away using coded acoustic waves. Control commands incorporating bio-inspired obstacle avoidance algorithms are sent in stages to guide the UAV to an intelligent recovery pod. This invention achieves accurate detection, intelligent identification, secure countermeasures, and reliable takeover of UAVs, enhancing airspace security capabilities.

Description

Technical Field

[0001] This invention belongs to the field of server heat dissipation technology, and in particular relates to a method for detecting and countering unmanned aerial vehicles (UAVs). Background Technology

[0002] Currently, drone detection and countermeasures technology has developed along multiple parallel technological paths, but it still faces many limitations in practical applications:

[0003] Limitations of detection methods: Traditional drone detection relies heavily on single-spectrum monitoring or optical imaging, which is insufficient to cover wide-band drone signals in complex electromagnetic environments. Furthermore, it has low accuracy in identifying drones with signal camouflage or material stealth capabilities. For example, drones using frequency-hopping communication or radar-absorbing materials can effectively evade single-band monitoring, leading to a high rate of missed detections.

[0004] Shortcomings in target identification and threat assessment: Existing identification technologies mostly rely on matching based on pre-set feature libraries. When facing new types of drones or camouflaged targets, they are prone to misjudgment due to a lack of adaptive learning capabilities. At the same time, threat assessments rely heavily on location information and are difficult to combine with the drone's physical parameters and behavioral patterns to conduct in-depth intent analysis, resulting in insufficient targeting of countermeasures.

[0005] The limited scope and intrusiveness of countermeasures: Current countermeasures primarily rely on electromagnetic interference or physical destruction. The former easily disrupts nearby civilian communications, while the latter can cause secondary damage after the drone crashes. Traditional electromagnetic interference is ineffective against high-end drones employing quantum-encrypted communication; and physical destruction is also unsuitable for targets requiring recovery and evidence collection.

[0006] Technical bottlenecks in takeover control: Existing drone takeover methods mostly rely on cracking the communication protocols of specific models, resulting in poor versatility and a lack of accurate modeling of the dynamic response of the drone flight control system. When the drone is in an interference environment or has hardware differences, takeover commands are prone to failure, making it difficult to smoothly guide the drone to the recovery area, especially in complex terrain where obstacle avoidance capabilities are insufficient.

[0007] The inefficiency of multi-technology collaboration: Detection, identification, countermeasures, and control are mostly independent systems, leading to delays in data transmission and decision-making coordination, making it difficult to deal with high-speed moving or dynamically evasive drone targets. For example, information asynchrony between the detection and countermeasure modules may result in delayed countermeasures, reducing the interception success rate.

[0008] Therefore, it is urgent to improve the existing drone detection and countermeasures methods to solve the aforementioned technical problems. Summary of the Invention

[0009] The purpose of this invention is to provide a method for detecting and countering unmanned aerial vehicles (UAVs) to achieve efficient handling of various types of UAVs throughout the entire process, ensuring airspace safety while taking into account the accuracy and security of countermeasures.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0011] A method for detecting and countering unmanned aerial vehicles (UAVs) includes the following steps:

[0012] S1: Deploy a multi-dimensional sensing network to perform instantaneous scanning of the entire 0.1-18GHz frequency band and simultaneously collect multi-dimensional features in the airspace, including noise characteristics of radio signals, material characteristics of the terahertz band, and Doppler effect characteristics.

[0013] S2: Map multi-dimensional features to a virtual twin, reconstruct the physical parameters, communication protocols and behavior patterns of the target drone through digital thread technology, and perform in-depth comparison with a camouflage sample library generated by an adversarial generative network. When the confidence level exceeds the dynamic threshold, a holographic target profile containing three-dimensional motion vectors, energy characteristics and potential threat intent is generated.

[0014] S3: By modulating the quantum entanglement signal in the specified frequency band, non-invasive interference is carried out on the quantum encrypted communication link of the UAV, a controllable plasma cloud is generated around the target UAV, selectively attenuating its navigation and image transmission signals, and determining whether the target UAV is controllable. If not, a coded sound wave matching the structural resonant frequency of the target UAV is emitted to achieve forced removal without physical damage. If yes, step S4 is executed.

[0015] S4: Activate the takeover system based on brain-computer interface mapping. By analyzing the electrical signal characteristics of the UAV flight control system, establish a neuromorphic mapping model of the control commands, and send control commands containing bio-inspired obstacle avoidance algorithms in stages to guide the target to the intelligent recovery pod.

[0016] Preferably, the multi-dimensional sensing network includes a metamaterial antenna array, a weak noise detection unit, and a terahertz imaging module. The specific process of the multi-dimensional sensing network performing instantaneous scanning of the entire frequency band and simultaneously acquiring the noise characteristics of radio signals in the spatial domain, the material characteristics of the terahertz band, and the Doppler effect characteristics in step S1 is as follows:

[0017] S11: Initialize the multi-dimensional sensing network. The metamaterial antenna array automatically adjusts the array aperture according to the preset monitoring range to form a three-dimensional beam topology structure that adapts to the full frequency band of 0.1-18GHz. The weak noise detection unit completes the noise reference calibration and establishes the identification baseline of weak signals at the -170dBm level. The terahertz imaging module is preheated to a stable working state, and the material feature sampling interval of the 0.3-3THz frequency band is set.

[0018] S12: Perform full-band instantaneous scanning: The metamaterial antenna array adopts spatial diversity technology to divide the 0.1-18GHz frequency band into 128 parallel detection channels. The synchronous excitation of each channel is achieved through photonic crystal delay lines to complete the full-band instantaneous scanning. The real-time signal preprocessing unit performs real-time Fourier transform on the received signal to extract frequency agility characteristics and channel occupancy patterns.

[0019] S13: Feature Acquisition and Synchronous Fusion: The noise spectrum of the signal is captured by the metamaterial antenna array. The target signal and environmental noise are separated by a vacuum noise comparison algorithm. The phase noise, amplitude fluctuation and frequency drift characteristics of the signal are recorded to generate a noise feature fingerprint database. The terahertz imaging module emits coherent terahertz waves and receives the echo signal after penetrating the target UAV. The characteristic absorption peaks of different materials are analyzed by time-domain terahertz spectroscopy. The dielectric constant distribution spectrum of the internal structure of the target UAV is reconstructed to distinguish between the payload and the airframe structure. The frequency shift generated by the moving target is measured by adopting the coherent Doppler radar principle. Combined with the phase difference calculation of multiple antennas, a three-dimensional motion vector is generated. The rotation frequency and blade number characteristics of the rotor and propeller are analyzed by micro-Doppler effect.

[0020] S14: Real-time data association and calibration: The time synchronization module uses an atomic clock to align the timestamps of multi-dimensional feature acquisitions, the spatial coordinate transformation unit maps the observation data from different sensors to a unified coordinate system, and the Kalman filter algorithm fuses and reduces noise from multi-dimensional features.

[0021] Preferably, the specific process of mapping multi-dimensional features to a virtual twin in step S2 and reconstructing the physical parameters, communication protocols, and behavioral patterns of the target UAV using digital thread technology is as follows:

[0022] S21: Deploy a digital thread system that includes a data middle platform, a time-series database, and distributed computing nodes. Use a Protobuf-based feature data encapsulation format to define data interaction standards and establish an end-to-end data link for perceptual features, virtual mapping, and attribute reconstruction.

[0023] S22: Synchronously access multi-dimensional feature data through the edge gateway interface of the digital thread system;

[0024] S23: Multi-feature cooperative mapping based on digital thread systems:

[0025] Mapping the noise characteristics of radio signals to communication attributes: transforming the noise fingerprint vector in the noise characteristics into the electromagnetic characteristics of a virtual twin;

[0026] A noise interference model for a virtual communication link is constructed by inferring the operating frequency band, transmission power, and modulation method of the communication module from the noise power spectrum.

[0027] Record the intermediate parameters of the feature mapping process to form a traceable communication feature mapping thread;

[0028] Material characteristics in the terahertz band are mapped to physical parameters: a digital thread-driven reverse modeling engine converts terahertz point cloud data into a 3D model of the target UAV, and determines the material properties of each component by matching the material absorption spectrum with a virtual material library.

[0029] Physical parameters are calculated based on material properties: the overall mass is calculated by density and volume, the maximum load is inferred by structural strength parameters, and the range is estimated by motor thermal radiation characteristics.

[0030] Doppler effect characteristics are mapped to the motion model: the motion parameters are input into the dynamics simulation module, and the real-time attitude of the virtual twin is calculated through the six-degree-of-freedom equation to reproduce the flight trajectory of the physical target;

[0031] Analyze the micro-Doppler modulation spectrum to extract propeller parameters and construct an output characteristic model of the virtual power system;

[0032] S24: Reconstruct the core attributes of the target drone.

[0033] Physical parameter reconstruction: Combining the material feature mapping results of the terahertz band, a set of physical properties including geometric parameters, mass parameters, and dynamic parameters is generated; the type of effective load is identified based on structural point cloud data, and the load weight is inferred from the volume to construct a physical performance boundary model of the virtual twin;

[0034] Communication protocol reconstruction: Deeply analyze noise characteristics, identify frequency hopping protocols through frequency jump patterns, and analyze data frame structure through noise burst cycles; combine a specified UAV communication protocol library, infer the target protocol type through feature matching, and deploy an equivalent protocol stack in a virtual twin;

[0035] Behavioral pattern reconstruction: The digital thread calls the time series analysis module to classify the behavior of the Doppler trajectory data and extract the feature parameters of each behavior.

[0036] Preferably, in step S2, the holographic target profile containing three-dimensional motion vectors, energy features, and potential threat intent is generated by deep comparison of the camouflage sample library generated by the adversarial generative network, and when the confidence level exceeds the dynamic threshold. The specific process is as follows:

[0037] S25: Deploy a multimodal adversarial generative network, which includes a generator and a discriminator. The generator adopts the DCGAN architecture; the discriminator uses a ResNet-50 network to distinguish between real samples and fake samples.

[0038] S26: Generate a fake sample library:

[0039] Input dimensions: Integrating the noise characteristics of radio signals, material characteristics of the terahertz band, and Doppler effect characteristics as conditional constraints;

[0040] Generate camouflage samples: 3D virtual samples including physical camouflage samples, signal camouflage samples, and behavioral camouflage samples;

[0041] Dynamic expansion of the camouflage sample library: After processing 100 real target cases, the GAN model is incrementally trained to incorporate newly discovered camouflage features into the generator.

[0042] S27: Perform a deep comparison of multi-dimensional features with a fake sample database:

[0043] Physical feature comparison: Calculate the geometric similarity between the target UAV 3D model and the physical camouflage sample using IoU;

[0044] Signal feature comparison: Cosine similarity is used to calculate the matching degree between the target noise fingerprint vector and the signal camouflage sample to identify frequency hopping pattern camouflage and power hiding features;

[0045] Behavioral feature comparison: The motion trajectory is converted into a behavioral embedding vector by an LSTM encoder and compared with behavioral spoofing samples in the sample library;

[0046] S28: Holographic Target Profile Generation and Threat Intent Assessment

[0047] Three-dimensional motion vector: Based on the Doppler effect feature analysis, a three-dimensional motion parameter set containing instantaneous velocity, acceleration, and angular velocity is generated, along with a motion trajectory prediction curve;

[0048] Energy characteristics: Battery capacity, remaining power, and driving time are estimated by inferring from terahertz thermal imaging characteristics, and energy replenishment demand levels are marked.

[0049] Potential threat intent assessment: Acquire behavioral parameters including the distance between the target UAV's flight trajectory and the designated sensitive area, its loitering time, and its movement patterns; analyze the correlation between the target UAV's behavior and preset threat cases through graph neural networks, and output threat intent labels, including reconnaissance, boundary crossing, and suspicious payload delivery. For target UAVs with high threat intent, automatically associate them with physical parameters including payload volume to generate a potential hazard assessment and obtain an intent assessment report.

[0050] Preferably, the specific process of step S3 is as follows:

[0051] S31: Target communication frequency band detection: Based on the UAV communication protocol parameters reconstructed by the virtual twin, determine the frequency band of the interfering target and simultaneously collect the quantum state characteristics of the target signal;

[0052] S32: Quantum Entanglement Signal Generation: The quantum signal generator generates entangled photon pairs through spontaneous parametric downconversion. One photon retains the local reference, while the other is modulated to the target communication frequency band through a tunable filter. Based on the principle of quantum teleportation, the quantum state characteristics of the local reference photon are mapped to the emitted photon, so that the interference signal and the quantum state of the target communication link are partially entangled. The interference signal adopts a pulse modulation mode.

[0053] S33: Non-invasive jamming implementation: A directional transmitting antenna focuses the quantum entangled signal onto the target UAV; the phase and amplitude of the jamming signal are adjusted in real time through quantum measurement feedback, reducing the quantum error rate of the target communication link from 10... -6 Level upgraded to 10 -2 Level; synchronously monitors the adaptive adjustment of the target communication link and dynamically updates interference parameters;

[0054] S34: Controllable Plasma Cloud Generation and Signal Attenuation Modulation:

[0055] Plasma cloud parameter planning:

[0056] The coverage area of ​​the plasma cloud is calculated based on the drone size of the holographic target file;

[0057] Based on the type of signal to be attenuated, determine the electron density and collision frequency of the plasma so that the attenuation rate of the cloud to the target frequency band is ≥30dB and the attenuation to other frequency bands is ≤5dB.

[0058] Plasma cloud formation and maintenance:

[0059] The microwave excitation device emits directional millimeter waves, which are focused on the air region around the target drone, generating plasma by ionizing air molecules;

[0060] Real-time monitoring of cloud density distribution; when the local density is below a threshold, the microwave excitation power of the corresponding area is automatically increased.

[0061] Selective signal attenuation verification:

[0062] The spectrum monitoring module synchronously collects the changes in the strength of the target UAV's navigation signal and image transmission signal to verify the attenuation effect;

[0063] S35: Determine the controllability of the target drone.

[0064] Communication link response test: Under the combined effect of quantum interference and plasma attenuation, a standardized control command probe packet is sent to the target UAV; the target's response signal is monitored, and signal analysis is used to determine whether its flight control system still maintains the ability to receive commands;

[0065] Dynamic state assessment: Based on Doppler effect characteristics and virtual twin model, analyze the motion stability of the target under disturbance environment; assess the state of the power system: monitor the temperature change of the motor through terahertz thermal imaging, and judge whether it is still in a controllable working state by combining speed fluctuations.

[0066] Controllability Quantitative Scoring: Evaluate communication responsiveness, motion stability, and power system status; each indicator has a full score of 100 points, and a comprehensive score of ≥70 points is considered controllable, 50-69 points is considered partially controllable, and <50 points is considered completely uncontrollable; for controllable targets, further match the corresponding takeover protocol for its model to provide a basis for subsequent takeover control.

[0067] Preferably, it also includes dynamically adjusting the countermeasure effect and setting a termination mechanism:

[0068] Continuously monitor the status of the target drone, and when the score drops below a preset threshold, automatically increase the intensity of quantum interference or expand the coverage area of ​​the plasma cloud;

[0069] If the target is determined to be completely out of control and the risk of falling is low, the countermeasure intensity is gradually reduced, linearly decaying to shutdown within 10 seconds.

[0070] Preferably, the specific process of activating the brain-computer interface-based takeover system in step S4, and establishing a neuromorphic mapping model of the control commands by analyzing the electrical signal characteristics of the UAV flight control system, is as follows:

[0071] S41: Brain-Computer Interface Mapping Takeover System Initialization:

[0072] Analysis of electrical signal characteristics of flight control system: Real-time electrical signal characteristics of the core control unit of the UAV flight control system are extracted by capturing communication data of the UAV flight control system during the quantum entanglement signal interference stage;

[0073] The electrical signal features are extracted using wavelet transform, and a mapping relationship library between electrical signal features and physical actions is established as a benchmark for subsequent instruction generation.

[0074] Takeover system hardware activation: Load the pre-trained UAV flight control system response model, initialize the communication interface, and activate the directional command transmission array;

[0075] A communication link is established with the intelligent recovery capsule to obtain the capsule's coordinates, door status, and a 3D map of the surrounding airspace.

[0076] S42: Construction of the neuromorphic mapping model for control commands:

[0077] Flight control system response characteristic modeling: Send 10 sets of calibration commands to the UAV, record the feedback characteristics of electrical signal features, and construct a command-response dynamic model through a Bayesian network;

[0078] Based on the physical parameters of the virtual twin, the model parameters are corrected to ensure that the deviation between the virtual simulation command response and the actual measurement is ≤3%.

[0079] The rules for generating neuromorphic control commands are set up as follows: a spiking neural network is used to simulate the firing mode of biological neurons, and traditional control commands are converted into spatiotemporally encoded pulse sequences; the Heb learning rule is introduced to make the command pulse sequence resonate with the characteristic frequency bands of the UAV's electrical signal characteristics;

[0080] Activate the brain-computer interface-based takeover system and establish a neuromorphic mapping model of control commands by analyzing the electrical signal characteristics of the UAV flight control system.

[0081] Preferably, the specific process of sending control commands containing bio-inspired obstacle avoidance algorithms in stages in step S4 to guide the target to the intelligent recovery pod is as follows:

[0082] S43: Embedding a bio-inspired obstacle avoidance algorithm:

[0083] Biological mechanism simulation of obstacle avoidance algorithm: Drawing on the swarm intelligence mechanism of bee swarm navigation, obstacles are identified through residual optical sensor data from drones or environmental data monitored on the ground, and collision risk is predicted by combining acceleration sensor data;

[0084] Simulates the echolocation principle of bats, generates virtual sound wave detection signals, calculates the distance to obstacles and avoidance angles, and outputs three basic obstacle avoidance actions: emergency pull-up, side-shift avoidance, and deceleration detour.

[0085] S44: Perform dynamic planning of the recovery path: Using the intelligent recovery pod as the endpoint, generate an initial 3D path using the A* algorithm; update the path based on real-time positioning data, and trigger local replanning when the deviation error is >3m; when the distance to the recovery pod is ≤100m, automatically switch to the spiral descent path mode;

[0086] S45: Phased control command transmission and status closure:

[0087] The first phase is the confirmation of takeover:

[0088] Send low-intensity neuromorphic commands to monitor the response characteristics of the UAV's electrical signals;

[0089] Gradually increase the complexity of instructions and preview the execution effect of instructions using a virtual twin;

[0090] If there is no response to a specified number of consecutive commands, the command strength will be automatically increased while maintaining the quantum interference at a low level.

[0091] The second stage is path guidance:

[0092] Send instructions according to waypoints along the planned route. Each waypoint instruction includes three-dimensional coordinates and arrival time limit.

[0093] Embedded bio-inspired obstacle avoidance algorithm: When an obstacle is detected, obstacle avoidance instructions are automatically inserted, and the original path is automatically returned after avoidance;

[0094] The third stage is docking with the recovery capsule: when the distance to the recovery capsule is specified, a hovering command is sent to activate the recovery capsule's laser positioning system;

[0095] Based on laser positioning data, refined control commands are sent to gradually bring the drone into the capture range of the recovery capsule;

[0096] After the drone enters the recovery capsule, it sends a command to shut down its power, which triggers the electromagnetic buffer device in the recovery capsule to generate a gradient magnetic field to achieve contactless deceleration and complete a soft landing.

[0097] The beneficial effects of this invention include:

[0098] The UAV detection and countermeasure method provided by this invention firstly deploys a multi-dimensional sensing network comprising a metamaterial antenna array, a weak noise detection unit, and a terahertz imaging module to achieve instantaneous scanning across the entire 0.1-18 GHz frequency band, simultaneously acquiring radio signal noise characteristics, terahertz material characteristics, and Doppler effect characteristics. Compared to traditional single-spectrum or optical detection methods, it covers a wider frequency band and offers richer feature dimensions. Combined with Kalman filter fusion noise reduction technology, it can effectively identify UAVs employing frequency-hopping communication and material camouflage, improving target acquisition rate, especially significantly enhancing the early detection capability for low-observable targets.

[0099] Secondly, by leveraging digital thread technology to map multi-dimensional features onto a virtual twin, the target's physical parameters, communication protocols, and behavioral patterns are reconstructed. This is then combined with a camouflage sample library from an adversarial generative network for in-depth comparison. This overcomes the limitations of traditional feature library matching by generating camouflage samples of various types, including physical, signal, and behavioral types, thereby improving the accuracy of camouflaged target identification. Simultaneously, based on the 3D motion vectors, energy characteristics, and threat intent analysis of holographic target profiles, an upgrade from target identification to threat cognition is achieved, providing decision-level support for countermeasure strategies.

[0100] Furthermore, the method employs a coordinated countermeasure approach that combines quantum entanglement signal interference with quantum encrypted communication links and controllable plasma clouds. Compared to traditional electromagnetic suppression or physical destruction methods, this non-invasive interference avoids physical damage to the drone, facilitating subsequent evidence collection or recovery. Selective attenuation technology reduces interference with surrounding civilian communications, improving electromagnetic compatibility. Combined with coded acoustic wave decoys, it achieves non-destructive forced decoys, reducing the risk of secondary crashes.

[0101] Finally, the brain-computer interface-based takeover system constructs a neuromorphic mapping model by analyzing electrical signal characteristics, enabling control commands to resonate with the flight control system in the same frequency band, thus shortening the takeover response delay. It also embeds a bio-inspired obstacle avoidance algorithm to improve the success rate of obstacle avoidance in complex terrain. The phased command transmission and docking with the intelligent recovery capsule enable the smooth soft landing of the UAV, significantly improving the recovery success rate. Attached Figure Description

[0102] Figure 1 This is a flowchart illustrating the drone detection and countermeasure method of the present invention.

[0103] Figure 2 This is a schematic diagram of the spatial diversity principle of the present invention.

[0104] Figure 3 This is a schematic diagram of the terahertz imaging module of the present invention. Detailed Implementation

[0105] The following is in conjunction with the appendix Figure 1~Figure 3 The present invention will be further described in detail below:

[0106] Example 1

[0107] See appendix Figure 1 As shown, a method for detecting and countering unmanned aerial vehicles (UAVs) includes the following steps:

[0108] S1: Deploy a multi-dimensional sensing network, which includes a metamaterial antenna array, a weak noise detection unit, and a terahertz imaging module. This network performs instantaneous scanning across the entire 0.1-18 GHz frequency band, simultaneously acquiring multi-dimensional features in the airspace, including noise characteristics of radio signals, material characteristics of the terahertz band, and Doppler effect characteristics. The metamaterial antenna array, based on artificially designed microstructures (metasurfaces, tunable elements, etc.), can precisely control the propagation path and phase changes of electromagnetic waves at the subwavelength scale, thereby dynamically adjusting the antenna radiation characteristics.

[0109] S2: Multi-dimensional features are mapped to a virtual twin. The physical parameters, communication protocols, and behavior patterns of the target UAV are reconstructed through digital thread technology. A deep comparison is performed with a camouflage sample library generated by an adversarial generative network. When the confidence level exceeds the dynamic threshold, a holographic target profile containing three-dimensional motion vectors, energy characteristics, and potential threat intent is generated.

[0110] S3: By modulating the quantum entanglement signal in the specified frequency band, non-invasive interference is carried out on the quantum encrypted communication link of the UAV, a controllable plasma cloud is generated around the target UAV, selectively attenuating its navigation and image transmission signals, and determining whether the target UAV is controllable. If not, a coded sound wave matching the structural resonant frequency of the target UAV is emitted to achieve forced removal without physical damage. If yes, step S4 is executed.

[0111] S4: Activate the takeover system based on brain-computer interface mapping. By analyzing the electrical signal characteristics of the UAV flight control system, establish a neuromorphic mapping model of the control commands, and send control commands containing bio-inspired obstacle avoidance algorithms in stages to guide the target to the intelligent recovery pod.

[0112] The specific process of the multi-dimensional sensing network instantaneously scanning the entire frequency band from 0.1 to 18 GHz in step S1, and simultaneously acquiring noise characteristics of radio signals, material characteristics of the terahertz band, and Doppler effect characteristics in the spatial domain, is as follows:

[0113] S11: Initialize the multi-dimensional sensing network. The metamaterial antenna array automatically adjusts the array aperture according to the preset monitoring range. The array aperture is continuously adjustable within the range of 1-5 meters to form a three-dimensional beam topology structure adapted to the 0.1-18GHz frequency band. The weak noise detection unit completes the noise reference calibration and establishes the identification baseline of weak signals at the -170dBm level. The terahertz imaging module is preheated to a stable working state, and the material feature sampling interval in the 0.3-3THz frequency band is set.

[0114] S12: Perform a full-band instantaneous scan: See Figure 2 As shown, the metamaterial antenna array uses spatial diversity technology to divide the 0.1-18GHz frequency band into 128 parallel detection channels. Synchronous excitation of each channel is achieved through photonic crystal delay lines, completing a 50ns-level instantaneous scan across the entire frequency band. The real-time signal preprocessing unit performs real-time Fourier transform on the received signal to extract frequency agility characteristics and channel occupancy patterns.

[0115] See Figure 2 As shown, the signals received by the n antennas in the antenna array cover the entire frequency band from 0.1 to 18 GHz. G1-Gn are broadband gain conditioning modules that perform gain compensation on the full-band signals received by each antenna to balance the receiving gain of different antennas and perform preliminary filtering to remove out-of-band interference, while retaining the entire frequency band from 0.1 to 18 GHz. The output is a full-band, multi-antenna parallel signal stream, which prepares for subsequent channelization.

[0116] The switching logic or demodulator integrates a channelizer to perform two key operations: frequency band segmentation (dividing the 0.1-18GHz band into sub-bands) and space-frequency band parallel processing (sending the signal streams from n antennas into a 128-channel processing link). In other words, through the switching logic and the channelization processing within the demodulator, the full-band signal from each antenna is divided into multiple sub-bands (channels). Utilizing the spatial parallelism of multiple antennas, 128-channel parallel detection across the entire frequency band is ultimately achieved.

[0117] S13: Feature Acquisition and Synchronous Fusion: The metamaterial antenna array captures the noise spectrum of the signal, separates the target signal from the environmental noise through a vacuum noise comparison algorithm, records the phase noise, amplitude fluctuations, and frequency drift characteristics of the signal, and generates a noise feature fingerprint database. The terahertz imaging module emits coherent terahertz waves and receives the echo signals after penetrating the target UAV. The characteristic absorption peaks of materials including plastics, metals, and composite materials are analyzed through time-domain terahertz spectroscopy technology to reconstruct the dielectric constant distribution spectrum of the internal structure of the target UAV, distinguishing between the payload and the airframe structure. Using the principle of coherent Doppler radar, the frequency shift generated by the moving target is measured with high precision. Combined with multi-antenna phase difference calculation, a three-dimensional motion vector including velocity, acceleration, and angular velocity is generated. The rotation frequency and blade number characteristics of the rotor and propeller are analyzed through the micro-Doppler effect.

[0118] See the schematic diagram of the terahertz imaging module. Figure 3 As shown, the laser generates ultrashort pulse laser light, providing a pump source for the excitation of terahertz waves. The laser is focused onto the emitter, and through photoconductive or optical rectification effects, the light pulse energy is converted into terahertz pulses, instantaneously exciting free carriers or polarization electric fields, radiating terahertz waves onto the sample. The terahertz detector converts the electric field changes of the terahertz waves into measurable optical signal changes through electro-optic sampling. A current preamplifier amplifies the weak current signal output from the detector. An A / D converter and digital signal processing convert the analog electrical signal into a digital signal. Through algorithms such as Fourier transform, denoising, and spectral analysis, the amplitude, phase, and spectral information of the terahertz waves are extracted, ultimately retrieving the optical parameters (refractive index, absorption coefficient) and structural image of the sample. A DC bias applies a DC voltage to the terahertz emitter to optimize the excitation of terahertz waves.

[0119] S14: Real-time data association and calibration: The time synchronization module achieves 1ns-level timestamp alignment of multi-dimensional feature acquisition through an atomic clock, the spatial coordinate transformation unit maps the observation data of different sensors to a unified coordinate system, and the Kalman filter algorithm fuses and reduces noise of multi-dimensional features to improve the detection sensitivity of low-speed / hovering targets.

[0120] The specific process of mapping multi-dimensional features to a virtual twin in step S2, and reconstructing the physical parameters, communication protocols, and behavioral patterns of the target UAV using digital thread technology, is as follows:

[0121] S21: Deploy a digital thread system that includes a data middle platform, a time-series database, and distributed computing nodes. Use a Protobuf-based feature data encapsulation format to define data interaction standards and establish an end-to-end data link for perceptual features, virtual mapping, and attribute reconstruction.

[0122] S22: Synchronously access multi-dimensional feature data through the edge gateway interface of the digital thread system;

[0123] Noise characteristics of radio signals: noise fingerprint vector, phase noise power spectrum, and time-series data of frequency drift coefficient;

[0124] Material characteristics in the terahertz band: three-dimensional structural point cloud, material absorption spectrum, dielectric constant distribution matrix;

[0125] Doppler effect characteristics include three-dimensional motion parameters such as velocity, acceleration, and angular velocity, micro-Doppler modulation spectrum, and trajectory coordinate sequence.

[0126] S23: Multi-feature cooperative mapping based on digital thread systems:

[0127] The noise characteristics of radio signals are mapped to communication attributes: the digital thread system calls the electromagnetic simulation module to convert the noise fingerprint vector into the electromagnetic characteristics of a virtual twin, and reproduces the hardware characteristics of the signal source in virtual space, including oscillator phase noise and power amplifier nonlinear distortion.

[0128] By inferring the operating frequency band, transmit power, and modulation methods (including AM / FM / FSK) of the communication module from the noise power spectrum, a noise interference model of the virtual communication link is constructed. The digital thread system records the intermediate parameters of the feature mapping process, including the simulation step size and electromagnetic environment coefficient, forming a traceable communication feature mapping thread.

[0129] Material characteristics in the terahertz band mapped to physical parameters:

[0130] A digital thread-driven reverse modeling engine transforms terahertz point cloud data into a 3D model of the target UAV, including the geometric parameters of internal structures such as motors, batteries, and circuit boards. It determines the material properties of each component by matching material absorption spectra with a virtual material library. Based on these material properties, physical parameters are calculated: the overall mass is calculated using density and volume; the maximum load is inferred from structural strength parameters; and the flight time is estimated using the motor's thermal radiation characteristics.

[0131] Doppler effect characteristics are mapped to the motion model: The digital thread system inputs motion parameters into the dynamics simulation module, calculates the real-time attitude of the virtual twin using six-degree-of-freedom equations, including pitch, roll, and yaw angles, and drives the digital model to reproduce the flight trajectory of the physical target. Micro-Doppler modulation spectra are analyzed to extract propeller parameters: number of blades, rotational speed, and pitch, constructing an output characteristic model of the virtual power system. The digital thread system records temporal correlation data of motion characteristics, including the causal relationship between changes in rotational speed, thrust, and acceleration, providing a dynamic basis for behavior pattern reconstruction.

[0132] S24: Reconstruct the core attributes of the target drone.

[0133] Physical parameter reconstruction: The digital thread fuses the mapping results of material characteristics in the terahertz band to generate a set of physical properties including geometric parameters, mass parameters, and dynamic parameters. The geometric parameters include length, wingspan, and altitude; the mass parameters include total mass and center of gravity position; and the dynamic parameters include maximum lift and endurance.

[0134] Based on structural point cloud data, the payload types, including cameras, batteries, and special equipment, are identified. The payload weight is inferred from the volume, and a physical performance boundary model of the virtual twin is constructed. The physical performance boundary includes the maximum rate of ascent and wind resistance level.

[0135] Communication Protocol Reconstruction: The digital thread system performs in-depth analysis of the noise characteristics of radio signals: frequency hopping protocols are identified through frequency hopping patterns, and the data frame structure, including frame length and interval time, is analyzed through noise burst cycles. Combining known UAV communication protocol libraries, including DJI Lightbridge and WiFi 802.11n, the target protocol type is inferred through feature matching, and an equivalent protocol stack is deployed in a virtual twin.

[0136] Behavioral pattern reconstruction: The digital thread system calls the timing analysis module to classify the Doppler trajectory data into behaviors, including cruising, hovering, circling, and diving, and extracts the feature parameters of each behavior, including the cruising speed range and the fluctuation amplitude of the hovering position.

[0137] Typical behavioral patterns are reproduced in a virtual twin: low-battery return flight behavior is simulated by adjusting virtual power system parameters, and no-fly zone avoidance behavior is simulated by modifying navigation logic.

[0138] In step S2, a deep comparison is performed using a camouflage sample library generated by an adversarial generative network. When the confidence level exceeds a dynamic threshold, a holographic target profile containing three-dimensional motion vectors, energy features, and potential threat intent is generated. The specific process is as follows:

[0139] S25: Deploy a multimodal adversarial generative network, which includes a generator and a discriminator. The generator adopts the DCGAN architecture; the discriminator adopts the ResNet-50 network and is responsible for distinguishing real samples from fake samples.

[0140] S26: Generate a fake sample library:

[0141] Input dimensions: The noise feature vector of the radio signal, the material characteristics of the terahertz band, and the Doppler motion parameter sequence are used as conditional constraints;

[0142] Generates a camouflage sample library: This library produces 3D virtual samples containing physical camouflage samples, signal camouflage samples, and behavioral camouflage samples. Physical camouflage samples include bird-like shapes and camouflage coatings; signal camouflage samples include frequency hopping concealment and noise camouflage; and behavioral camouflage samples include mimicking civil aviation flight paths and hovering camouflage. The camouflage sample library is categorized and stored according to camouflage type, including physical camouflage, signal camouflage, and behavioral camouflage libraries, with a total capacity dynamically maintained at around 100,000 samples, supporting fast retrieval.

[0143] S27: Deep comparison of multi-dimensional features with a faked sample database:

[0144] Physical feature comparison: The geometric similarity between the target's 3D model and the physical camouflage samples in the camouflage sample library is calculated by IoU (Intersection over Union). The comparison focuses on camouflage parts, including radar stealth design at the wing edges.

[0145] Signal feature comparison: Cosine similarity is used to calculate the matching degree between the target noise fingerprint vector and the signal camouflage sample to identify features such as frequency hopping pattern camouflage and power hiding;

[0146] Behavioral Feature Comparison: The motion trajectory is converted into a behavioral embedding vector using an LSTM encoder and compared with behavioral camouflage samples in the sample library, including reconnaissance camouflage and evasion camouflage. The LSTM encoder is the encoding part of an autoencoder implemented using a Long Short-Term Memory network, used to compress the input motion trajectory data into a fixed-length vector representation. The specific process is as follows:

[0147] An LSTM encoder mainly consists of an embedding layer and an LSTM layer. The embedding layer converts each coordinate point in the motion trajectory into a fixed-dimensional embedding vector, typically with dimensions of 128 or 256. The LSTM layer processes the sequence of embedding vectors, extracting long-term dependency features through the synergistic effect of forget gates, input gates, and output gates, generating hidden states and cell states. The hidden states and cell states output by the encoder are finally integrated into a behavior embedding vector. This vector contains the global motion features of the trajectory, including velocity and acceleration.

[0148] S28: Holographic Target Profile Generation and Threat Intent Assessment

[0149] Three-dimensional motion vector: Based on the Doppler effect feature analysis, a three-dimensional motion parameter set containing instantaneous velocity, acceleration, and angular velocity is generated, along with a motion trajectory prediction curve;

[0150] Energy characteristics: Battery capacity, remaining power, and driving time are estimated by inferring from terahertz thermal imaging characteristics, and energy replenishment demand levels are marked, including high, medium, and low demand levels.

[0151] Potential Threat Intent Assessment: This involves acquiring behavioral parameters, including the distance between the target UAV's flight path and designated sensitive areas, its loitering time, and its movement patterns. Designated sensitive areas include military bases and nuclear power plants, and movement patterns include low-altitude penetration and high-altitude reconnaissance. A Graph Neural Network (GNN) is used to analyze the correlation between the target UAV's behavior and preset threat cases, outputting threat intent labels. These labels include reconnaissance, boundary crossing, and suspicious payload delivery. For high-threat-intent target UAVs, physical parameters, including payload volume, are automatically associated to generate a potential hazard assessment and a threat intent assessment report.

[0152] The specific process of step S3 is as follows:

[0153] S31: Target communication frequency band detection: Based on the UAV communication protocol parameters reconstructed by the virtual twin, such as the working frequency band and modulation method of the quantum encryption link, determine the interference target frequency band, which is usually the 1-6GHz quantum encryption communication common frequency band. Simultaneously collect the quantum state characteristics of the target signal, including photon polarization direction and entanglement parameters.

[0154] S32: Quantum Entanglement Signal Generation: A quantum signal generator produces entangled photon pairs through spontaneous parametric down-conversion (SPDC). One photon retains the local reference, while the other is modulated to the target communication frequency band through a tunable filter. Based on the principle of quantum teleportation, the quantum state characteristics of the local reference photon are mapped to the emitted photon, causing the interference signal to form partial entanglement with the quantum state of the target communication link. The entanglement degree is controlled at 0.3-0.5 to avoid completely disrupting communication. The interference signal adopts a pulse modulation mode, with the pulse width (10-100ns) matching the target communication frame structure to ensure that the interference energy is concentrated during the data transmission period.

[0155] S33: Non-intrusive jamming implementation: A directional transmitting antenna focuses the quantum entangled signal onto the target UAV with a beamwidth ≤3° to avoid interfering with surrounding equipment; the phase and amplitude of the jamming signal are adjusted in real time through quantum measurement feedback, reducing the quantum error rate of the target communication link from 10... -6 Level upgraded to 10 -2 Level 1: Only affects data integrity, does not physically damage equipment; synchronously monitors the adaptive adjustment of the target communication link, including frequency hopping and increasing encryption strength, and dynamically updates interference parameters.

[0156] S34: Controllable Plasma Cloud Generation and Signal Attenuation Modulation:

[0157] Plasma cloud parameter planning:

[0158] Based on the size of the drone according to the holographic target profile, such as a wingspan of 0.5-2m, the coverage area of ​​the plasma cloud is calculated, with a diameter of 1.5 times the target size, to ensure complete coverage of the target.

[0159] Based on the type of signal to be attenuated: navigation signals: 1.575GHz GPS, 1.602GHz Beidou; image transmission signals: 2.4 / 5.8GHz, determine the electron density and collision frequency of the plasma so that the cloud attenuation rate of the target frequency band is ≥30dB and the attenuation of other frequency bands is ≤5dB.

[0160] The attenuation of electromagnetic waves by plasma mainly stems from absorption loss and reflection loss, and its attenuation characteristics are related to the frequency of the electromagnetic wave. f Plasma electron density n e Collision frequency of electrons and neutral particles n Closely related.

[0161] The relative complex permittivity of plasma is :e r = 1 −ω p 2 / ( n 2 + oh 2 ) −jν·ω p 2 / [ oh; ( n 2 + oh 2 )];

[0162] in, ω=2πf , oh The angular frequency of electromagnetic waves. oh p The plasma angular frequency, f The frequency of electromagnetic waves, n e For plasma electron density, n The collision frequency between electrons and neutral particles.

[0163] The attenuation rate is the attenuation coefficient of electromagnetic waves propagating in plasma. α calculate:

[0164] Attenuation rate = 20 log 10 (E0 / E)=20log10(e αL )≈8.686 αL ;

[0165] Where E0 represents the electric field strength before the electromagnetic wave enters the plasma cloud, i.e., the initial electric field strength, and E represents the electric field strength after the electromagnetic wave passes through the plasma cloud, i.e., the transmitted electric field strength. Since the electric field strength satisfies E=E0e^(-E / E) when the electromagnetic wave propagates in a lossy medium,... −αL After deformation, we get E0 / E=e αL .

[0166] Plasma cloud formation and maintenance:

[0167] The microwave excitation device emits directional millimeter waves of 30-100 GHz, focusing them on the air region surrounding the target UAV, generating plasma by ionizing air molecules. Magnetic confinement technology (magnetic field strength 0.1-0.5T) is used to maintain the cloud's shape and prevent rapid diffusion.

[0168] The density distribution of clouds is monitored in real time through terahertz imaging feedback. When the local density is lower than the threshold, the microwave excitation power of the corresponding area is automatically increased.

[0169] Selective signal attenuation verification: The spectrum monitoring module synchronously collects the changes in the strength of the target UAV's navigation signal and image transmission signal to verify the attenuation effect and ensure that the attenuation rate for civilian communication frequency bands (900MHz GSM, 1.8GHz LTE) is ≤10% to avoid secondary interference.

[0170] S35: Determine the controllability of the target drone.

[0171] Communication link response test: Under the combined effect of quantum interference and plasma attenuation, standardized control command probe packets are sent to the target UAV, which can include hovering, altitude reduction, and other commands conforming to common UAV command formats. The target's response signals, including ACK confirmation frames and attitude adjustments, are monitored. Signal analysis is used to determine whether its flight control system still maintains its command reception capability.

[0172] Dynamic state assessment: Based on Doppler effect characteristics and a virtual twin model, analyze the target's motion stability under disturbance conditions: such as whether there is irregular drift with displacement >5m / 10s, or attitude loss of control with yaw angle fluctuation >15°. Assess the power system status: Monitor motor temperature changes through terahertz thermal imaging, and combine this with speed fluctuations to determine whether it is still in a controllable operating state; speed stability ≥80% is considered normal.

[0173] Controllability Quantitative Scoring: Communication responsiveness (40% weight), motion stability (30% weight), and power system status (30% weight) are scored. Each indicator has a maximum score of 100 points, and a comprehensive score is obtained based on a weighted summation method. A score ≥ 70 points is considered controllable, a score between 50 and 69 points indicates limited controllability, and a score < 50 points indicates complete loss of control. For controllable targets, the corresponding takeover protocol for the model is further matched, including the DJI SDK interface and the general MAVLink protocol, to provide a basis for subsequent takeover control.

[0174] It also includes dynamically adjusting the countermeasures and setting a termination mechanism:

[0175] The system continuously monitors the target drone's status. When the score drops below a threshold, it automatically increases the intensity of quantum interference or expands the coverage area of ​​the plasma cloud. If the target is determined to be completely out of control and has a low risk of crashing, the countermeasure intensity is gradually reduced, linearly decaying to shutdown within 10 seconds to avoid wasting resources.

[0176] In step S4, the takeover system based on brain-computer interface mapping is activated. The specific process of establishing a neuromorphic mapping model of control commands by analyzing the electrical signal characteristics of the UAV flight control system is as follows:

[0177] S41: Brain-Computer Interface Mapping Takeover System Initialization:

[0178] Analysis of electrical signal characteristics of flight control system:

[0179] By capturing communication data of the UAV flight control system during the quantum entanglement signal interference stage, real-time electrical signal characteristics of the core control unit, including the MCU and sensor fusion module, are extracted, including pulse width modulation (PWM) signal sequence, sensor data interaction frequency, and control command check code pattern.

[0180] These electrical signal features are extracted using wavelet transform, including the 10-20Hz characteristic frequency band corresponding to height adjustment and the 20-50Hz characteristic frequency band for direction control. A mapping relationship library between electrical signal features and physical actions is established, such as a specific pulse sequence corresponding to climbing action, which serves as the benchmark for subsequent command generation.

[0181] Takeover system hardware activation:

[0182] Load the pre-trained UAV flight control system response model, initialize the communication interface, activate the directional command transmission array, and ensure that the alignment accuracy between the command transmission beam and the UAV receiving antenna is ≤0.5° to reduce the signal attenuation impact of residual plasma clouds. Simultaneously establish a communication link with the intelligent recovery capsule to acquire the capsule's coordinates, door status, and a 3D map of the surrounding airspace, including no-fly zones and obstacle data.

[0183] S42: Construction of the neuromorphic mapping model for control commands:

[0184] Flight control system response characteristic modeling: Send 10 sets of calibration commands to the UAV, such as small climb and left turn, and record the feedback characteristics of electrical signal features, including response delay and signal amplitude changes. Construct a command-response dynamic model through a Bayesian network.

[0185] Configure rules for generating neuromorphic control commands:

[0186] By using a spiking neural network (SNN) to simulate the firing pattern of biological neurons, the traditional control command with a height of 10m is replaced with a spatiotemporally encoded pulse sequence, and the height increases with three pulses within 50ms.

[0187] The Heb learning rule is introduced to make the command pulse sequence resonate with the characteristic frequency band of the UAV's electrical signal characteristics, such as matching the frequency of the command pulse for steering control with the characteristic frequency band of the flight control system's directional control.

[0188] A brain-computer interface-based takeover system is activated. By analyzing the electrical signal characteristics of the UAV flight control system, a neuromorphic mapping model of control commands is established. The input layer receives preprocessed flight control electrical signal characteristics, with each feature corresponding to an input neuron in the model. The output layer corresponds to the UAV's control commands, with each command corresponding to an output neuron. The activation state of the neuron represents the trigger probability of the command.

[0189] Referring to the hierarchical structure of biological neural networks, a hidden layer is designed: Hidden layer neurons simulate the information integration function of the cerebral cortex, receiving input signals through synaptic connections and performing nonlinear processing on the signal characteristics. Synaptic weights are set based on the correlation strength between electrical signal characteristics and control commands; for example, signals of a certain frequency are more likely to trigger turning commands. Initial synaptic weights are assigned, and subsequently dynamically adjusted through learning.

[0190] The specific process of sending control commands containing bio-inspired obstacle avoidance algorithms in stages in step S4 to guide the target to the intelligent recovery pod is as follows:

[0191] S43: Embedding a bio-inspired obstacle avoidance algorithm:

[0192] Biological mechanism simulation of obstacle avoidance algorithm: Drawing on the swarm intelligence mechanism of bee swarm navigation, obstacles are identified by using optical sensor data left by drones or environmental data monitored on the ground, and collision risk is predicted by combining acceleration sensor data.

[0193] Simulating the echolocation principle of bats, virtual sound wave detection signals are generated, and obstacle distance and avoidance angle are calculated based on the principle of minimum energy consumption.

[0194] Based on the biological characteristics of bat echolocation, key parameters for virtual acoustic detection signals were designed:

[0195] Frequency range: Select 10kHz~100kHz to balance detection accuracy and propagation distance. High-frequency signals are more accurate in positioning but attenuate faster, while low-frequency signals propagate further.

[0196] Waveform type: Pulsed ultrasound is used, with a typical pulse width of 0.5~5ms. The pulse interval is dynamically adjusted according to the environment, with a longer interval in open areas and a shorter interval in complex environments.

[0197] Amplitude modulation: The initial transmitted signal amplitude is fixed, and the echo signal amplitude attenuates with the propagation distance, which conforms to the attenuation law of spherical waves: the amplitude is inversely proportional to the distance.

[0198] It simulates the ability of bats to adaptively adjust signals according to the environment: when there are no obstacles, it emits low-frequency, long-pulse-interval signals to reduce energy consumption; when a suspected obstacle is detected, it automatically switches to high-frequency, short-pulse-interval signals to improve positioning accuracy.

[0199] By analyzing the difference between the echo and the emitted signal, the distance to the obstacle can be calculated. The core idea is to utilize the relationship between time difference and wave speed. In the virtual scene, it is assumed that a detector (simulating a bat's ear) receives the echo signal and simultaneously records the arrival time, frequency shift, and amplitude of the echo.

[0200] Distance calculation principle: The core formula based on bat echolocation is: Obstacle distance = Sound wave propagation speed × Echo time difference / 2. Where, echo time difference = Echo arrival time - Signal transmission time.

[0201] The speed of sound propagation is approximately 343 m / s in air. If the time difference between the emitted signal and the echo is 10 ms, then the distance = (343 m / s × 0.01 s) / 2 ≈ 1.715 m.

[0202] Calculate the avoidance angle based on the principle of minimum energy consumption:

[0203] When bats avoid obstacles, they tend to choose the path with the lowest energy consumption, such as the smallest turning angle and the shortest detour distance. They need to optimize the angle by combining the location of the obstacle with their own movement.

[0204] Determining the azimuth of obstacles: Analyzing the differences in echoes using a multi-channel virtual auditory sensor (simulating the binaural effect of bats).

[0205] Time difference positioning: The azimuth angle θ is calculated by combining the time difference Δt between the arrival of the echo at the left and right sensors with the velocity of sound: θ = arcsin(velocity of sound × Δt / distance between the two sensors).

[0206] Intensity difference positioning: When an obstacle deviates from the centerline, the echo intensity received by the nearby sensor is higher. The azimuth angle is corrected by the intensity difference, and finally the azimuth angle of the obstacle relative to the direction of movement of the detector is obtained, such as 30° to the left and 5° directly in front.

[0207] The calculation logic for the minimum energy consumption avoidance angle: Energy consumption is positively correlated with the complexity of the motion trajectory. For example, sharp turns consume more energy than small-angle turns, and the following conditions must be met:

[0208] Constraints: The avoidance path must be far away from the obstacle, i.e., the safe distance ≥ obstacle radius + detector size;

[0209] Optimization objective: Minimize the turning angle, i.e., minimize Δθ, and return to the original target direction after detouring. For example, if the original plan was to fly to point A, it can still reach the target efficiently after detouring.

[0210] Specific process:

[0211] Set the current direction of motion of the detector as the reference (0°), and the azimuth angle of the obstacle as θ, which can be set to 20° to the left.

[0212] Calculate the minimum turning angle for safe detour: If the obstacle is on the left, turn to the right by Δθ. Δθ must be ≥ θ + safety redundancy angle, and Δθ should be as small as possible. For example, if the obstacle is 20° on the left, the minimum turning angle can be 25°, which avoids the obstacle and reduces energy consumption.

[0213] If the obstacle is directly in front, i.e., θ≈0°, then choose the smallest left / right turn angle, such as ±30°. Prioritize the side with the smaller deviation from the original target direction. For example, if the original target is on the right, prioritize turning to the right.

[0214] S44: Perform dynamic planning of the recovery path: Using the intelligent recovery pod as the endpoint, use the A* algorithm to generate an initial three-dimensional path, which includes 5-8 waypoints and a path safety redundancy of ≥5m, that is, the minimum distance to obstacles is 5m;

[0215] The path is updated every 500ms based on real-time positioning data, which is either ground radar or residual GPS signal from the UAV. Local replanning is triggered when the deviation error is greater than 3m. When the distance to the recovery capsule is ≤100m, the path automatically switches to a spiral descent mode with a pitch of 20m and a radius of 10m to reduce the impact of airflow interference.

[0216] S45: Phased control command transmission and status closure:

[0217] The first phase is the confirmation of takeover:

[0218] Send low-intensity neuromorphic commands, such as maintaining the current altitude, monitor the response characteristics of the drone's electrical signals, and confirm that the command reception success rate is ≥90%.

[0219] By gradually increasing the complexity of the instructions, small-angle turns can be achieved. The execution effect of the instructions can be pre-visualized using a virtual twin to ensure that the actual action deviation is ≤5°.

[0220] If there is no response to three consecutive commands, the command strength will be automatically increased and the pulse amplitude will be increased by 20%, while maintaining the quantum interference at a low intensity to avoid completely blocking communication.

[0221] The second phase is path guidance, with the duration dynamically adjusted based on distance: instructions are sent to waypoints along the planned path, each waypoint instruction including 3D coordinates and an arrival time limit. A bio-inspired obstacle avoidance algorithm is embedded: when an obstacle is detected, obstacle avoidance instructions are automatically inserted, and the system automatically returns to the original path after avoidance. The execution effect of instructions is evaluated in real time: the deviation between the actual position and the waypoint is calculated using the Doppler effect, and a correction instruction is sent when the deviation exceeds 10m.

[0222] The third stage is docking with the recovery capsule: When the drone is at a designated distance from the recovery capsule, a hovering command is sent to activate the capsule's laser positioning system. Based on the laser positioning data, refined control commands are sent, such as a descent rate of 0.5 m / s and a lateral fine-tuning of 0.3 m, to gradually bring the drone into the recovery capsule's capture range. After the drone enters the recovery capsule, a power shutdown command is sent, triggering the capsule's electromagnetic buffer device to generate a gradient magnetic field for contactless deceleration, completing a soft landing.

Claims

1. A method for detecting and countering unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: S1: Deploy a multi-dimensional sensing network to perform instantaneous scanning of the entire 0.1-18GHz frequency band and simultaneously collect multi-dimensional features in the airspace, including noise characteristics of radio signals, material characteristics of the terahertz band, and Doppler effect characteristics. S2: Map multi-dimensional features to a virtual twin, reconstruct the physical parameters, communication protocols and behavior patterns of the target drone through digital thread technology, and perform in-depth comparison with a camouflage sample library generated by an adversarial generative network. When the confidence level exceeds the dynamic threshold, a holographic target profile containing three-dimensional motion vectors, energy characteristics and potential threat intent is generated. S3: By modulating the quantum entanglement signal in the specified frequency band, non-invasive interference is carried out on the quantum encrypted communication link of the UAV, a controllable plasma cloud is generated around the target UAV, selectively attenuating its navigation and image transmission signals, and determining whether the target UAV is controllable. If not, a coded sound wave matching the structural resonant frequency of the target UAV is emitted to achieve forced removal without physical damage. If yes, step S4 is executed. S4: Activate the takeover system based on brain-computer interface mapping. By analyzing the electrical signal characteristics of the UAV flight control system, establish a neuromorphic mapping model of control commands, and send control commands containing bio-inspired obstacle avoidance algorithms in stages to guide the target to the intelligent recovery pod. The multi-dimensional sensing network includes a metamaterial antenna array, a weak noise detection unit, and a terahertz imaging module. The specific process of the multi-dimensional sensing network performing instantaneous scanning of the entire frequency band and simultaneously acquiring the noise characteristics of radio signals in the spatial domain, the material characteristics of the terahertz band, and the Doppler effect characteristics in step S1 is as follows: S11: Initialize the multi-dimensional sensing network. The metamaterial antenna array automatically adjusts the array aperture according to the preset monitoring range to form a three-dimensional beam topology structure that adapts to the full frequency band of 0.1-18GHz. The weak noise detection unit completes the noise reference calibration and establishes the identification baseline of weak signals at the -170dBm level. The terahertz imaging module is preheated to a stable working state, and the material feature sampling interval of the 0.3-3THz frequency band is set. S12: Perform full-band instantaneous scanning: The metamaterial antenna array adopts spatial diversity technology to divide the 0.1-18GHz frequency band into 128 parallel detection channels. The synchronous excitation of each channel is achieved through photonic crystal delay lines to complete the full-band instantaneous scanning. The real-time signal preprocessing unit performs real-time Fourier transform on the received signal to extract frequency agility characteristics and channel occupancy patterns. S13: Feature Acquisition and Synchronous Fusion: The noise spectrum of the signal is captured by the metamaterial antenna array. The target signal and environmental noise are separated by a vacuum noise comparison algorithm. The phase noise, amplitude fluctuation and frequency drift characteristics of the signal are recorded to generate a noise feature fingerprint database. The terahertz imaging module emits coherent terahertz waves and receives the echo signal after penetrating the target UAV. The characteristic absorption peaks of different materials are analyzed by time-domain terahertz spectroscopy. The dielectric constant distribution spectrum of the internal structure of the target UAV is reconstructed to distinguish between the payload and the airframe structure. The frequency shift generated by the moving target is measured by adopting the coherent Doppler radar principle. Combined with the phase difference calculation of multiple antennas, a three-dimensional motion vector is generated. The rotation frequency and blade number characteristics of the rotor and propeller are analyzed by micro-Doppler effect. S14: Real-time data association and calibration: The time synchronization module uses an atomic clock to align the timestamps of multi-dimensional feature acquisitions, the spatial coordinate transformation unit maps the observation data from different sensors to a unified coordinate system, and the Kalman filter algorithm fuses and reduces noise from multi-dimensional features.

2. The method for detecting and countering unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The specific process of mapping multi-dimensional features to a virtual twin in step S2, and reconstructing the physical parameters, communication protocols, and behavioral patterns of the target UAV using digital thread technology, is as follows: S21: Deploy a digital thread system that includes a data middle platform, a time-series database, and distributed computing nodes. Use a Protobuf-based feature data encapsulation format to define data interaction standards and establish an end-to-end data link for perceptual features, virtual mapping, and attribute reconstruction. S22: Synchronously access multi-dimensional feature data through the edge gateway interface of the digital thread system; S23: Multi-feature cooperative mapping based on digital thread systems: Mapping the noise characteristics of radio signals to communication attributes: transforming the noise fingerprint vector in the noise characteristics into the electromagnetic characteristics of a virtual twin; A noise interference model for a virtual communication link is constructed by inferring the operating frequency band, transmission power, and modulation method of the communication module from the noise power spectrum. Record the intermediate parameters of the feature mapping process to form a traceable communication feature mapping thread; Material characteristics in the terahertz band are mapped to physical parameters: a digital thread-driven reverse modeling engine converts terahertz point cloud data into a 3D model of the target UAV, and determines the material properties of each component by matching the material absorption spectrum with a virtual material library. Physical parameters are calculated based on material properties: the overall mass is calculated by density and volume, the maximum load is inferred by structural strength parameters, and the range is estimated by motor thermal radiation characteristics. Doppler effect characteristics are mapped to the motion model: the motion parameters are input into the dynamics simulation module, and the real-time attitude of the virtual twin is calculated through the six-degree-of-freedom equation to reproduce the flight trajectory of the physical target; Analyze the micro-Doppler modulation spectrum to extract propeller parameters and construct an output characteristic model of the virtual power system; S24: Reconstruct the core attributes of the target drone. Physical parameter reconstruction: Combining the material feature mapping results of the terahertz band, a set of physical properties including geometric parameters, mass parameters, and dynamic parameters is generated; the type of effective load is identified based on structural point cloud data, and the load weight is inferred from the volume to construct a physical performance boundary model of the virtual twin; Communication protocol reconstruction: Deeply analyze noise characteristics, identify frequency hopping protocols through frequency jump patterns, and analyze data frame structure through noise burst cycles; combine a specified UAV communication protocol library, infer the target protocol type through feature matching, and deploy an equivalent protocol stack in a virtual twin; Behavioral pattern reconstruction: The digital thread calls the time series analysis module to classify the behavior of the Doppler trajectory data and extract the feature parameters of each behavior.

3. The method for detecting and countering unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, In step S2, a deep comparison is performed using a camouflage sample library generated by an adversarial generative network. When the confidence level exceeds a dynamic threshold, a holographic target profile containing three-dimensional motion vectors, energy features, and potential threat intent is generated. The specific process is as follows: S25: Deploy a multimodal adversarial generative network, which includes a generator and a discriminator. The generator adopts the DCGAN architecture; the discriminator uses a ResNet-50 network to distinguish between real samples and fake samples. S26: Generate a fake sample library: Input dimensions: Integrating the noise characteristics of radio signals, material characteristics of the terahertz band, and Doppler effect characteristics as conditional constraints; Generate camouflage samples: 3D virtual samples including physical camouflage samples, signal camouflage samples, and behavioral camouflage samples; Dynamic expansion of the camouflage sample library: After processing 100 real target cases, the GAN model is incrementally trained to incorporate newly discovered camouflage features into the generator. S27: Perform a deep comparison of multi-dimensional features with a fake sample database: Physical feature comparison: Calculate the geometric similarity between the target UAV 3D model and the physical camouflage sample using IoU; Signal feature comparison: Cosine similarity is used to calculate the matching degree between the target noise fingerprint vector and the signal camouflage sample to identify frequency hopping pattern camouflage and power hiding features; Behavioral feature comparison: The motion trajectory is converted into a behavioral embedding vector by an LSTM encoder and compared with behavioral spoofing samples in the sample library; Adversarial verification: The discriminator classifies a mixture of multi-dimensional features and fake samples, and outputs a truth score for the multi-dimensional features; When the score is less than 70, it is determined that the target may be disguised, and the enhanced comparison mode is activated to increase the sample database search volume by 30%. S28: Holographic Target Profile Generation and Threat Intent Assessment Three-dimensional motion vector: Based on the Doppler effect feature analysis, a three-dimensional motion parameter set containing instantaneous velocity, acceleration, and angular velocity is generated, along with a motion trajectory prediction curve; Energy characteristics: Battery capacity, remaining power, and driving time are estimated by inferring from terahertz thermal imaging characteristics, and energy replenishment demand levels are marked. Potential threat intent assessment: Acquire behavioral parameters including the distance between the target UAV's flight trajectory and the designated sensitive area, its dwell time, and its movement patterns; Analyze the correlation between the target UAV's behavior and preset threat cases through graph neural networks, and output threat intent labels, including reconnaissance, boundary crossing, and suspicious payload delivery. For target UAVs with high threat intent, automatically associate them with physical parameters including payload volume to generate a potential hazard assessment and obtain an intent assessment report.

4. The method for detecting and countering unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The specific process of step S3 is as follows: S31: Target communication frequency band detection: Based on the UAV communication protocol parameters reconstructed by the virtual twin, determine the frequency band of the interfering target and simultaneously collect the quantum state characteristics of the target signal; S32: Quantum entangled signal generation: The quantum signal generator generates entangled photon pairs through spontaneous parametric downconversion, in which one beam of photons retains the local reference, and the other beam is modulated to the target communication frequency band through a tunable filter; Based on the principle of quantum teleportation, the quantum state characteristics of the local reference photon are mapped to the emitted photon, so that the interference signal and the quantum state of the target communication link are partially entangled; the interference signal adopts a pulse modulation mode. S33: Non-invasive jamming implementation: A directional transmitting antenna focuses the quantum entangled signal onto the target UAV; the phase and amplitude of the jamming signal are adjusted in real time through quantum measurement feedback, reducing the quantum error rate of the target communication link from 10... -6 Level upgraded to 10 -2 Level; synchronously monitors the adaptive adjustment of the target communication link and dynamically updates interference parameters; S34: Controllable Plasma Cloud Generation and Signal Attenuation Modulation: Plasma cloud parameter planning: The coverage area of ​​the plasma cloud is calculated based on the drone size of the holographic target file; Based on the type of signal to be attenuated, determine the electron density and collision frequency of the plasma so that the attenuation rate of the cloud to the target frequency band is ≥30dB and the attenuation to other frequency bands is ≤5dB. Plasma cloud formation and maintenance: The microwave excitation device emits directional millimeter waves, which are focused on the air region around the target drone, generating plasma by ionizing air molecules; Real-time monitoring of cloud density distribution; when the local density is below a threshold, the microwave excitation power of the corresponding area is automatically increased. Selective signal attenuation verification: The spectrum monitoring module synchronously collects the changes in the strength of the target UAV's navigation signal and image transmission signal to verify the attenuation effect; S35: Determine the controllability of the target drone. Communication link response test: Under the combined effect of quantum interference and plasma attenuation, a standardized control command probe packet is sent to the target UAV; the target's response signal is monitored, and signal analysis is used to determine whether its flight control system still maintains the ability to receive commands; Dynamic state assessment: Based on Doppler effect characteristics and virtual twin model, analyze the motion stability of the target under disturbance environment; assess the state of the power system: monitor the temperature change of the motor through terahertz thermal imaging, and judge whether it is still in a controllable working state by combining speed fluctuations. Controllability Quantitative Scoring: Evaluate communication responsiveness, motion stability, and power system status; each indicator has a full score of 100 points, and a comprehensive score of ≥70 points is considered controllable, 50-69 points is considered partially controllable, and <50 points is considered completely uncontrollable; for controllable targets, further match the corresponding takeover protocol for its model to provide a basis for subsequent takeover control.

5. The method for detecting and countering unmanned aerial vehicles (UAVs) according to claim 4, characterized in that, It also includes dynamically adjusting the countermeasures and setting a termination mechanism: Continuously monitor the status of the target drone, and when the score drops below a preset threshold, automatically increase the intensity of quantum interference or expand the coverage area of ​​the plasma cloud; If the target is determined to be completely out of control and the risk of falling is low, the countermeasure intensity is gradually reduced, linearly decaying to shutdown within 10 seconds.

6. The method for detecting and countering unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, In step S4, the takeover system based on brain-computer interface mapping is activated. The specific process of establishing a neuromorphic mapping model of control commands by analyzing the electrical signal characteristics of the UAV flight control system is as follows: S41: Brain-Computer Interface Mapping Takeover System Initialization: Analysis of electrical signal characteristics of flight control system: Real-time electrical signal characteristics of the core control unit of the UAV flight control system are extracted by capturing communication data of the UAV flight control system during the quantum entanglement signal interference stage; The electrical signal features are extracted using wavelet transform to establish a mapping relationship library between electrical signal features and physical actions, which serves as the benchmark for subsequent instruction generation. Takeover system hardware activation: Load the pre-trained UAV flight control system response model, initialize the communication interface, and activate the directional command transmission array; A communication link is established with the intelligent recovery capsule to obtain the capsule's coordinates, door status, and a 3D map of the surrounding airspace. S42: Construction of the neuromorphic mapping model for control commands: Flight control system response characteristic modeling: Send 10 sets of calibration commands to the UAV, record the feedback characteristics of electrical signal features, and construct a command-response dynamic model through a Bayesian network; Based on the physical parameters of the virtual twin, the model parameters are corrected to ensure that the deviation between the virtual simulation command response and the actual measurement is ≤3%. The rules for generating neuromorphic control commands are set up as follows: a spiking neural network is used to simulate the firing mode of biological neurons, and traditional control commands are converted into spatiotemporally encoded pulse sequences; the Heb learning rule is introduced to make the command pulse sequence resonate with the characteristic frequency bands of the UAV's electrical signal characteristics; Activate the brain-computer interface-based takeover system and establish a neuromorphic mapping model of control commands by analyzing the electrical signal characteristics of the UAV flight control system.

7. A method for detecting and countering unmanned aerial vehicles (UAVs) according to claim 6, characterized in that, The specific process of sending control commands containing bio-inspired obstacle avoidance algorithms in stages in step S4 to guide the target to the intelligent recovery pod is as follows: S43: Embedding a bio-inspired obstacle avoidance algorithm: Biological mechanism simulation of obstacle avoidance algorithm: Drawing on the swarm intelligence mechanism of bee swarm navigation, obstacles are identified through residual optical sensor data from drones or environmental data monitored on the ground, and collision risk is predicted by combining acceleration sensor data; Simulates the echolocation principle of bats, generates virtual sound wave detection signals, calculates the distance to obstacles and avoidance angles, and outputs three basic obstacle avoidance actions: emergency pull-up, side-shift avoidance, and deceleration detour. S44: Perform dynamic planning of the recovery path: Using the intelligent recovery pod as the endpoint, generate an initial 3D path using the A* algorithm; update the path based on real-time positioning data, and trigger local replanning when the deviation error is >3m; when the distance to the recovery pod is ≤100m, automatically switch to the spiral descent path mode; S45: Phased control command transmission and status closure: The first phase is the confirmation of takeover: Send low-intensity neuromorphic commands to monitor the response characteristics of the UAV's electrical signals; Gradually increase the complexity of instructions and preview the execution effect of instructions using a virtual twin; If there is no response to a specified number of consecutive commands, the command strength will be automatically increased while maintaining the quantum interference at a low level. The second stage is path guidance: Send instructions according to waypoints along the planned route. Each waypoint instruction includes three-dimensional coordinates and arrival time limit. Embedded bio-inspired obstacle avoidance algorithm: When an obstacle is detected, obstacle avoidance instructions are automatically inserted, and the original path is automatically returned after avoidance; The third stage is the docking of the recovery capsule: When the distance to the recovery capsule is specified, a hovering command is sent to activate the recovery capsule's laser positioning system; Based on laser positioning data, refined control commands are sent to gradually bring the drone into the capture range of the recovery capsule; After the drone enters the recovery capsule, it sends a command to shut down its power, which triggers the electromagnetic buffer device in the recovery capsule to generate a gradient magnetic field to achieve contactless deceleration and complete a soft landing.

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