Anti-unmanned aerial vehicle system and method based on multi-mechanism synergy damage of femtosecond laser

Abstract

The present application belongs to the technical field of low altitude defense, and particularly relates to a method for anti-UAV based on femtosecond laser multi-mechanism cooperative damage, and the specific steps are as follows: obtaining the characteristics and trajectory parameters of the low altitude moving target, completing classification and threat level determination; predicting the motion trend, and focusing the laser spot on the target surface according to the prediction results by the reflector cooperating with the beam pointing control system; performing adaptive optical compensation work, and adjusting the laser emission parameters according to the target parameter information; the femtosecond laser emits laser according to the set parameters, and the beam is focused after the aiming system and acts on the target to damage it; determining the damage result according to the data change degree of the target spot position and the motion trajectory, and determining whether to perform secondary strike based on the damage determination. The present application takes electromagnetic damage and material ionization destruction as the core, realizes efficient damage through three mechanisms, has strong environmental adaptability, low collateral damage and low strike cost.

Description

Technical Field

[0001] This invention belongs to the field of low-altitude defense technology, specifically relating to an anti-drone system and method based on femtosecond laser multi-mechanism synergistic destruction. Background Technology

[0002] Counter-drone methods refer to a defense system that uses technical means to detect, identify, interfere with, control, or physically destroy unauthorized or threatening drones; its core function is to mitigate the security risks caused by drones and maintain airspace order and asset security.

[0003] Problems with existing technology: Regarding damage efficiency, traditional laser systems require 2-5 seconds to shoot down drones and must target critical parts; electronic jamming can only interfere but cannot completely destroy them, and is ineffective against autonomous navigation drones; kinetic interception requires 2-13 minutes to respond, which is extremely inefficient. Regarding environmental adaptability, traditional laser systems suffer from 50-70% scattering attenuation in rain and fog, and are significantly affected by turbulence; electronic interference experiences severe scattering attenuation in urban environments, and has a high false alarm rate under electromagnetic interference; while kinetic interception is less affected by weather, it is not suitable for low-altitude, short-range operations. Regarding collateral damage, traditional laser systems cause significant collateral damage (5-10mm heat-affected zone), which can easily lead to fires; electronic interference causes no collateral damage, but it is not destructive; kinetic energy interception causes extremely large collateral damage (explosion fragments can reach hundreds of meters). Regarding usage costs, the electricity cost for a single traditional strike is approximately 10 yuan, and maintenance costs are high; the cost of a single electronic jamming device is approximately 10 yuan, but its anti-jamming capability is weak; the cost of kinetic energy interception is enormous and unsustainable. Summary of the Invention

[0004] The purpose of this invention is to provide an anti-drone system and method based on femtosecond laser multi-mechanism synergistic destruction, which can achieve efficient destruction through three major mechanisms with electromagnetic damage and material ionization as the core. It has strong environmental adaptability, low collateral damage and low attack cost.

[0005] The specific technical solution adopted by this invention is as follows: The anti-drone method based on femtosecond laser multi-mechanism coordinated destruction has the following specific steps: Multiple sensors work together to acquire the characteristics and trajectory parameters of low-altitude moving targets, and the AI ​​model completes classification and threat level determination based on the target parameter information. The target trajectory parameters are specifically optimized and the motion trend is predicted. The reflector, together with the beam pointing control system, focuses the laser spot onto the target surface according to the prediction results. Perform adaptive optics compensation by adjusting the laser emission parameters based on the target parameter information; The femtosecond laser emits laser light according to set parameters. After being focused by the aiming system, the beam acts on the target and destroys it. The time of impact is adjusted according to the target material. The damage result is determined based on the changes in the target's spot position and trajectory data, and a decision is made on whether to execute a secondary strike based on the damage determination.

[0006] Furthermore, in the multi-sensor collaborative operation, millimeter-wave radar is used for initial long-range detection, infrared thermal imager is used to capture target thermal features, visible light camera is used to collect detailed features, and lidar is used to obtain three-dimensional coordinates.

[0007] Furthermore, the target features and trajectory parameters are input into a hierarchical classification model, the classification result is output based on the target feature parameters, and the threat parameters are quantified based on the target trajectory parameters; The classification results and threat parameters are input into a dynamic decision tree model, which outputs the threat level and generates response instructions.

[0008] Furthermore, the adaptive optics compensation includes using a wavefront sensor to detect wavefront distortion caused by atmospheric turbulence, and a deformable mirror dynamically adjusting the laser wavefront based on the distortion data to compensate for laser beam diffusion. It also includes using self-focusing pulses excited by femtosecond lasers, combined with pulse amplification technology to suppress nonlinear effects, in order to reduce rain and fog scattering attenuation.

[0009] Furthermore, the specific process of emitting the laser is as follows: The optical fiber seed source generates the basic pulse with the following parameters: wavelength 1560nm, pulse width 100fs, repetition frequency 60MHz, average power 10mW, and single pulse energy 0.17nJ. By introducing negative dispersion through grating pairs or prism pairs, fiber stretchers can stretch the pulse width from 100 fs to 500 ps. First, the signal is amplified by an optical fiber amplifier, and then a low-temperature cooled solid-state amplifier is used to improve efficiency, ultimately outputting a pulse with a single pulse energy of 30mJ and a repetition frequency of 1kHz. By using a vacuum grating to compensate for positive dispersion and a nonlinear compressor to compress the pulse width to ≤300fs, the peak power reaches 100GW and above. The optical path offset is corrected using a fast-reflecting mirror, the pulse envelope is adjusted using an acousto-optic modulator, the spectral phase is optimized using a spatial light modulator, and the optical components are adjusted to M... 2 ≤1.3, final output peak power ≥100GW, M 2 Femtosecond lasers with a diameter of ≤1.3 ms.

[0010] Furthermore, the specific process of damaging the target is as follows: The laser is focused on the surface of the drone target and creates an ultra-strong electric field in an extremely small area, which strips the outer electrons of the material atoms and forms plasma. The plasma expands violently in a short period of time, forming a strong electromagnetic pulse with a wide frequency and high peak value, which then acts on the electronic components inside the drone. Ultrashort pulses inject energy into the target in an extremely short time, creating tiny craters only at the focal point.

[0011] Furthermore, the specific process for determining the damage is as follows: Monitor the surface condition of the target, detect structural anomalies based on the YOLOv7 model, and simultaneously overlay infrared auxiliary data to annotate the laser ablation area; By analyzing point cloud data to determine the structural integrity and spatial position changes of the target, and comparing the differences in point cloud density before and after damage, the trajectory of the fallen drone is updated. If the damage threshold is not reached, the system automatically adjusts the aiming point and laser parameters for a second strike. If the target has been damaged, the system returns to standby mode to wait for the next target, supporting continuous interception of multiple targets.

[0012] An anti-drone system based on femtosecond laser multi-mechanism coordinated destruction, used to execute anti-drone methods, the anti-drone system comprising: The target detection and identification module is used to enable long-range detection, classification, identification, and threat level determination of low-altitude UAVs; A high-precision dynamic aiming module is used to track moving targets and precisely focus the laser onto the target surface; An ultrafast laser emission module is used to generate and directionally emit femtosecond laser pulses with high repetition rate and high peak power. An adaptive optics compensation module is used to counteract the effects of environmental factors such as atmospheric turbulence, rain, and fog on laser transmission. It integrates atmospheric turbulence compensation technology, which uses a wavefront sensor to detect wavefront distortion in real time during laser transmission and dynamically adjusts the laser wavefront through a deformable mirror to compensate for beam deflection caused by turbulence. It utilizes the self-focusing pulse effect, which uses the high energy of an ultrafast laser to excite the air lens effect, so that the laser forms a self-focusing pulse during transmission, reducing scattering attenuation in rain and fog. The system control and feedback module is used for fully automated control of the entire process, real-time monitoring of the strike effect and adjustment of parameters.

[0013] Furthermore, the target detection and recognition module integrates multi-sensor fusion detection and AI visual compensation technology, wherein the multi-sensor fusion detection integrates millimeter-wave radar, infrared thermal imager, visible light camera and lidar; AI visual compensation technology employs an adaptive tracking algorithm to specifically optimize the typical motion trajectory of drones and predict the target's motion trend in real time.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of an anti-drone method.

[0015] The technical effects achieved by this invention are as follows: (1) The femtosecond laser system provided by the present invention can force the chemical bonds of the target material to break and the original physical structure to collapse instantly. Unlike traditional lasers, it does not need to wait for energy accumulation to achieve thermal melting. Therefore, the strike time can be shortened to less than 0.2 seconds. Moreover, it does not need to accurately aim at key parts such as batteries and flight control. It can effectively damage any area of ​​the UAV.

[0016] (2) The femtosecond laser system provided by the present invention has strong environmental adaptability, reduces rain and fog scattering attenuation, and the range is only reduced by 20-30% in moderate rain / light fog environment. It can also detect wavefront distortion caused by atmospheric turbulence and compensate for laser beam diffusion.

[0017] (3) The femtosecond laser system provided by the present invention can form a tiny crater only at the focal point, and the destruction is highly precise. This "point strike" mode not only ensures the damage effect, but also minimizes collateral damage, making it safe to use in sensitive scenarios such as airports and densely populated areas.

[0018] (4) The anti-drone method provided by the present invention has low electricity cost per strike, low annual maintenance cost, and also has the function of multi-target strike. Attached Figure Description

[0019] Figure 1 This is a flowchart of the anti-drone method provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0021] As attached Figure 1 As shown, the anti-drone method based on femtosecond laser multi-mechanism cooperative destruction has the following specific steps: Step 1: Target Detection and Threat Identification (0-3 seconds) Multi-sensor collaborative operation: millimeter-wave radar achieves initial detection at a distance of ≥3km, locking onto the trajectory of low-altitude moving targets; infrared thermal imager captures target thermal features in nighttime / low-visibility environments; visible light camera collects detailed features; and lidar acquires three-dimensional coordinates. Furthermore, data fusion algorithms are used to eliminate false alarms and missed detections from a single sensor; Specifically: High-precision clock synchronization technology (such as PTP protocol) is used to ensure that the data acquisition time error of radar, infrared, visible light, and lidar is ≤1ms. The coordinate transformation matrix is ​​used to unify the data of each sensor to the same spatial coordinate system (such as ENU coordinate system). Then, IMU (Inertial Measurement Unit) data is combined to correct the coordinate offset caused by UAV maneuvering to ensure the consistency of target position. Millimeter-wave radar (operating frequency band 30-300MHz) detects low-speed targets through Doppler frequency shift, but is susceptible to ground clutter interference. Noise is filtered through the CFAR (constant false alarm rate) algorithm to output a preliminary target point cloud; infrared thermal imager extracts target thermal radiation features (such as motor heating points) and effectively identifies low-contrast targets in night / foggy environments; visible light camera identifies detailed features such as rotor shape and fuselage texture through the YOLOv7 model; lidar generates a 3D point cloud through ToF (time of flight) ranging to accurately calculate target size and trajectory; If the radar detects a target but the optoelectronic system fails to acquire it, a Bayesian inference model is activated to calculate the confidence level. If the confidence level is lower than the threshold, it is marked as a false alarm. Conversely, a motion consistency check is performed on the target detected solely by the optoelectronic system. The outputs of each sensor are converted into probability distributions, fused, and a comprehensive confidence level is generated. For example, if the radar detection confidence level is 0.8 and the infrared confidence level is 0.7, then a fused confidence level ≥ 0.9 is required to determine it as a real target. It also employs Kalman filtering to update the sensor error model in real time (such as infrared temperature drift and laser refraction offset) to adapt to complex environments such as rain, fog, and strong light; and dynamically adjusts the sensor weights according to environmental conditions (such as raising the infrared weight to 0.9 in rainy and foggy weather and lowering it to 0.3 in visible light).

[0022] Furthermore, the AI ​​model completes target classification (civilian / military) and threat level determination, with an accuracy rate of ≥95%. Specifically: extract the frequency hopping mode of the drone's remote control signal (such as the 2.4GHz / 5.8GHz dual-frequency switching commonly used in the DJI Phantom series), and generate a spectral feature vector by combining it with SDR (Software Defined Radio); collect rotor noise through a microphone array, convert it through Mel-frequency cepstral coefficients (MFCC), and input it into a ResNet-18 network to distinguish between consumer-grade and military drones; generate joint features by combining visible light images and lidar point clouds through a PointFusion network to identify the drone type (such as quadcopter vs. fixed-wing) and payloads (such as abnormal payload bay dimensions); Load the hierarchical classification model to perform first-level classification (civilian / military): Input multimodal feature vectors and motion parameters (velocity, acceleration, track angle); use lightweight XGBoost ensemble learning to distinguish between consumer-grade (DJI), industry-grade (Matrice series), and military-grade ("Wing Loong" features), and output "civilian (confidence ≥ 0.95)", "suspected military (confidence 0.7-0.94)", and "confirmed military (confidence ≥ 0.95)". Perform Level 2 threat assessment: quantify threat parameters, including behavioral risks (intrusion height (≤50m high risk), speed (>20m / s attack tendency), trajectory (approaching the core area)); payload characteristics (infrared identification of heat sources (suspected explosives), lidar scanning of volume density (high-density metal indicates warhead)). A dynamic decision tree model is used, inputting "classification result + threat parameters + environmental context (e.g., whether it is a no-fly zone)" into the model. Based on a rule engine (e.g., CLIIPS) and LSTM time series analysis, it outputs a 4-level threat level, including: Level 1 (Low): Mistakenly enters civilian aircraft, triggering laser strobe to drive it away; Level 2 (Medium): Modifies drones, initiating electromagnetic interference; Level 3 (High): Military reconnaissance aircraft, authorized laser blinding; Level 4 (Lethal): Suicide attack, activating destructive laser.

[0023] Step 2: Dynamic tracking and aiming (≤1ms) High-precision dynamic aiming module activated: AI adaptive tracking algorithm based on deep learning, specifically optimized for drone trajectories such as constant speed, variable speed, and maneuvering avoidance, and predicts motion trends in real time; Specifically, based on historical trajectory data and real-time motion parameters (speed, acceleration, heading angle), the AI ​​model (LSTM network) predicts the target position within the next 0.5 seconds, with the trajectory prediction error controlled within ±0.5cm.

[0024] Furthermore, the piezoelectric ceramic driven fast reflector (response time ≤ 1ms) combined with the beam pointing control system can accurately focus the laser spot on the target surface with a tracking error ≤ 0.1mrad, and can stably cover the damaged area even when the target is traveling at 200km / h. Specifically, the two-axis servo turntable (bandwidth ≥ 50Hz) generates turntable deflection commands based on the radar / photoelectric target coordinates, and adjusts the azimuth and elevation angles through a PID controller, achieving a tracking accuracy of 0.1mrad; the fast reflector (FSM, resonant frequency ≥ 2kHz) uses high frame rate miss information (1000fps) from a visible light camera to achieve nanometer-level displacement compensation with a piezoelectric ceramic actuator, realizing micro-arc-level correction and ensuring that the light spot locks onto the target's core components (such as motors or batteries). It is worth noting that the fast mirror (FSM) adopts a flexible hinge drive structure to avoid mechanical friction, with a displacement resolution of 5nm and an angle deflection range of ±5mrad; the back of the mirror integrates a microchannel cooling system, which controls the mirror surface temperature rise (ΔT≤1℃) through circulating refrigerant to avoid beam distortion caused by thermal deformation; Furthermore, a multi-beam linkage strategy is implemented: for cluster targets, the system allocates multiple FSM units to work synchronously, and splits a single laser source into multiple outputs through an optical beam splitter. Each output independently targets different drones, achieving "one source, multiple targets" strike. In actual tests, it can intercept 10 drones at the same time, with a damage response time of ≤2 seconds.

[0025] Step 3: Environmental Compensation and Parameter Adjustment (Real-time) Specifically, the adaptive optics compensation module operates in real time: the wavefront sensor detects wavefront distortion caused by atmospheric turbulence at a frequency of ≥1kHz, and the deformable mirror dynamically adjusts the laser wavefront using the Zernike polynomial algorithm to compensate for laser beam diffusion. Actual measurements show that the compensation efficiency is ≥80%. The self-focusing pulse is formed by utilizing the air lens effect excited by the high energy of the femtosecond laser to reduce rain and fog scattering attenuation. Combined with chirped pulse amplification technology to suppress nonlinear effects, and dispersion pre-compensation technology to solve the pulse broadening problem, the range is reduced by only 20-30% in moderate rain / light fog conditions (compared to 50-70% for traditional lasers). Furthermore, the system adjusts the pulse repetition frequency (1-10kHz) and scanning path to disperse energy deposition points, and dynamically optimizes the emission strategy by combining aerosol monitoring to address the beam deflection problem caused by thermal corona generated by the interaction of high-energy lasers with air; it also simultaneously adjusts parameters such as laser repetition frequency and pulse energy to adapt to the target material and distance.

[0026] Step 4: Laser emission and precision strike (0.2-10 seconds) Specifically, the femtosecond laser emits laser light according to set parameters: a fiber seed source generates a 100 fs fundamental pulse, which is then broadened (500 ps), cryogenically amplified (-40°C to -20°C), compressed (within 300 fs), and shaped (M... 2 ≤1.3) and other processes, outputting laser with a peak power of over 100GW; Please refer to Table 1 below for the key parameters: Table 1 Based on the parameter information in Table 1, the following needs to be described in detail: The fiber seed source generates the basic pulse: passive mode-locking technology (such as nonlinear polarization rotation or saturable absorber) is used to achieve longitudinal mode phase synchronization in erbium-doped / ytterbium-doped fiber to generate the initial femtosecond pulse; generation parameters: wavelength 1560nm (or 780nm), pulse width 100fs, repetition frequency 60MHz, average power 10mW, and single pulse energy 10mW / 60MHz≈0.17nJ; Pulse broadening: By introducing negative dispersion through grating pairs or prism pairs, fiber broadeners can broaden the pulse width from 100 fs to 500 ps, ​​thus avoiding damage to optical components during amplification. Low-temperature amplification: First, the amplification is achieved through an optical fiber amplifier to 500kHz, 1W, and 2μJ. Then, a low-temperature cooled solid-state amplifier (Yb:CaF2 / Yb:YAG) (-40℃ to -20℃) is used to improve efficiency, ultimately outputting a 30mJ, 1kHz pulse. Pulse compression: Using a vacuum grating to compensate for positive dispersion, the power is compressed to within 300 fs by a nonlinear compressor, with a peak power of 30 mJ / 300 fs = 100 GW or more; Beam shaping: Fast mirror (FSM) corrects optical path offset; Acousto-optic modulator (AOM) adjusts pulse envelope to suppress amplified spontaneous emission (ASE); Spatial light modulator (SLM) optimizes spectral phase and enhances focusing intensity; Optical components adjust M... 2 ≤1.3, ensuring focused beam quality, with a final output peak power ≥100GW, M 2 Femtosecond lasers with a diameter of ≤1.3 ms; Furthermore, after being focused by the aiming system, the beam acts on the target, achieving damage through three core mechanisms, as follows: (1) Strong field ionization and material structure disintegration The core advantage of femtosecond lasers lies in their ultra-high peak power (typically ≥10). 11 W, far exceeding the 10 W of traditional lasers 6 When a laser (on the order of W) is focused on the surface of a drone target, it creates an extremely strong electric field in a tiny area (spot diameter ≤ 5mm @ 2km), far exceeding the Coulomb force between atoms in the material. This strong field directly strips the outer electrons from the atoms of the material, causing the target surface (including plastic shells, metal fuselages, electronic component packaging, etc.) to undergo an instantaneous avalanche ionization, forming plasma. In this process, the chemical bonds of the material are forcibly broken, and the physical structure collapses instantaneously without the need for heat accumulation; adaptation to the ionization threshold of different materials: plastic shell (10 9 -10 11 W / cm 2 ), metal casing (10 8 -10 10 W / cm 2The system's peak power density can be easily covered; During ionization, the chemical bonds of the material are forcibly broken, and the original physical structure collapses instantly. Unlike traditional lasers, there is no need to wait for energy accumulation to achieve thermal melting. Therefore, the strike time can be shortened to less than 0.2 seconds. Moreover, there is no need to precisely target key parts such as batteries and flight controls. Effective damage can be achieved by striking any area of ​​the drone.

[0027] (2) Electromagnetic pulse and electronic system paralysis When a femtosecond laser interacts with a target material, the plasma generated by ionization expands violently in a short time, forming a wide-frequency, high-peak electromagnetic pulse (EMP). This pulse has a wide frequency range and high peak power, and can penetrate the gaps in the UAV shell to act on internal electronic components, including flight control systems, navigation modules, communication chips, sensors, etc. Electromagnetic pulses can cause electronic components to malfunction, such as electrical breakdown or surge burnout. For example, the circuit nodes of the flight control chip may be broken down by instantaneous high voltage, resulting in the loss of signal processing capability; the antenna matching circuit of the navigation module may be interfered with, making it unable to receive GPS signals; and the sensor signal amplifier may be impacted by surges, resulting in the output of incorrect data. This electromagnetic damage is fundamental and can cause the drone to lose control instantly and fall from the sky, avoiding the secondary risks caused by battery fires due to the thermal effect of traditional lasers.

[0028] (3) Micro-nano scale structure destruction Femtosecond lasers have extremely short pulse widths, with ultrashort pulses of 100-300 fs that inject energy into the target in a very short time. The heat does not have time to diffuse (the thermal diffusion length is much smaller than the spot diameter), and only a tiny crater is formed at the focal point. The heat-affected zone is ≤100μm (1 / 50-1 / 100 of that of traditional lasers). This kind of micro-nano scale destruction is highly precise: for the power system of drones, it can precisely destroy the insulation layer of the motor coil, causing the motor to short-circuit and stop; for propellers, it can form micro-cracks at the root of the blades, causing them to break during high-speed rotation; for optical sensors (such as cameras), it can precisely damage the lens coating or photosensitive chip, causing them to lose their detection capability. This "point-strike" mode ensures both the destructive effect and minimizes collateral damage, making it safe for use in sensitive locations such as airports and densely populated areas.

[0029] Furthermore, the single-target strike time is adjusted according to the material: 0.2 seconds for metal casings; 3-10 seconds for composite materials; Parameters are matched according to the material: metal shells use 1-10kHz low repetition rate and 30mJ high single pulse energy; composite materials use 10-100kHz high repetition rate and 10-20mJ pulse energy. Distance adaptation: within 1km, use an average power of 1kW and a diameter of 280mm; within 2km, increase to an average power of 1.5kW, with adaptive compensation. Environmental adaptation: Use standard parameters on sunny days; increase average power by 10-20% and enable self-focusing pulse mode on rainy or foggy days.

[0030] Step 5: Feedback and Closed-Loop Optimization (Real-Time) LiDAR and visible light cameras monitor target status in real time: damage effect is determined by changes in light spot position and abnormal target movement trajectory (such as uncontrolled fall), with an assessment accuracy of ≥90%; Specifically, the visible light camera monitors the surface condition of the target during the damage process. It detects structural anomalies in the target (such as wing breakage or smoke) based on the YOLOv7 model, with a classification confidence level of ≥95%. At the same time, infrared auxiliary data is superimposed to mark the laser ablation area (triggered by red highlighting when the temperature is >800℃). The lidar analyzes the structural integrity and spatial position changes of the target through point cloud data. Specifically, it compares the difference in point cloud density before and after damage. If the local point cloud missing rate is greater than 30%, it is judged as "structural damage". It calculates the target's outer envelope volume in real time. If the volume reduction is greater than 15%, it triggers a "target failure" alarm. For UAVs that have been damaged and fallen, the trajectory is updated through Kalman filtering with an accuracy of ±0.2m (actual data). It also supports the simultaneous analysis of the trajectories of 5 fragments to avoid missing targets after swarm attacks. Furthermore, if the damage threshold is not reached, the system automatically adjusts the aiming point and laser parameters (such as increasing the repetition frequency) for a second strike. If the target has been damaged, it returns to standby mode to wait for the next target, supporting continuous interception of multiple targets. Specifically: visible light detects carbonization of the surface coating, and the volume reduction detected by lidar is <5% → triggering a supplementary laser; visible light detects wing breakage, and the lidar point cloud missing rate is >40% → terminate the attack and mark the mission as complete. Simultaneously, an adaptive adjustment strategy is adopted to dynamically allocate laser power according to the distribution of debris, prioritizing the strike on targets that still pose a threat (such as drones that have not yet crashed); and beam distortion caused by thermal turbulence generated by target combustion is corrected by deformable mirrors to maintain beam focus.

[0031] An anti-drone system based on femtosecond laser multi-mechanism coordinated destruction, the anti-drone system comprising: The target detection and identification module is used to achieve long-range detection, classification, identification, and threat level determination of low-altitude UAVs. The detection range is ≥3km; the identification accuracy is ≥95% (for common civilian / military UAVs); and the target detection response time is ≤3s.

[0032] The high-precision dynamic aiming module is used to track moving targets and precisely focus the laser onto the target surface. The aiming accuracy is ±0.05mrad; the tracking error is ≤0.1mrad (for moving targets with a speed ≤200km / h); and the tracking response time is ≤1ms. Furthermore, drones are characterized by their small size, high speed, and high maneuverability, making traditional tracking and aiming technologies insufficient for precision strikes. This system employs a "multi-sensor fusion + AI vision compensation" approach to achieve stable tracking of dynamic targets. Specifically, it also includes: (1) Multi-sensor fusion detection: It integrates millimeter-wave radar, infrared thermal imager, visible light camera and lidar. The millimeter-wave radar realizes long-distance initial detection (≥3km), the infrared thermal imager identifies targets in night / low visibility environment, the visible light camera captures detailed features, and the lidar obtains the three-dimensional coordinates of the target. The data fusion algorithm eliminates false alarms and missed detections of single sensors, and the recognition accuracy is ≥95%. (2) AI adaptive tracking algorithm: Based on deep learning training target tracking model, it is specifically optimized for the typical motion trajectory of UAV (uniform speed, variable speed, maneuver avoidance), and can predict the target motion trend in real time with a tracking error ≤0.1mrad; (3) Fast pointing control: The fast reflector driven by piezoelectric ceramics has a response time of ≤1ms. Combined with the laser beam pointing control system, the laser spot is accurately focused on the target surface. Even if the target is maneuvering at high speed (200km / h), the spot can stably cover the damaged area.

[0033] The ultrafast laser emission module is used to generate and directionally emit femtosecond laser pulses with high repetition rate and high peak power, wherein the peak power is ≥100GW; the pulse width is 100-300fs; the repetition rate is 1-10kHz; the average power is 1kW; and the effective strike radius is 0-2km. Furthermore, the high-power, high-stability femtosecond laser is the core of the system. Traditional single architectures (fiber or solid-state) struggle to balance power and pulse quality. This system adopts a hybrid architecture of "fiber seed source + solid-state amplification + cryogenic cooling," solving the international challenge of mass production of kilowatt-level high-power femtosecond lasers. The advantages of this architecture are: an average power of only 1kW (compared to over 50kW for traditional lasers), significantly reduced energy consumption, and substantial reduction in size and weight, enabling deployment in various scenarios such as vehicle-mounted and fixed installations. The cryogenic cooling technology solves the thermal distortion problem in high-power amplification, allowing the system to operate continuously around the clock.

[0034] An adaptive optics compensation module is used to counteract the effects of environmental factors such as atmospheric turbulence, rain, and fog on laser transmission. The atmospheric turbulence compensation efficiency is ≥80%, and the energy attenuation in rainy and foggy weather is controlled at 20-30% (compared to 50-70% for traditional lasers). Furthermore, when lasers propagate through the atmosphere, they are affected by atmospheric turbulence, rain, fog, dust, and other factors, leading to beam divergence and energy attenuation. Traditional laser systems experience a significant decrease in effectiveness under adverse weather conditions. This system addresses this issue through two core technologies: (1) Atmospheric turbulence compensation: The integrated adaptive optics system detects the wavefront distortion during laser transmission in real time through the wavefront sensor (detection frequency ≥1kHz), and dynamically adjusts the laser wavefront through the deformable mirror to compensate for the beam offset caused by turbulence, thereby improving the focusing accuracy by more than 80%. (2) Self-focusing pulse effect: The high energy of ultrafast lasers excites the air lens effect, enabling the laser to form a self-focusing pulse during transmission, reducing scattering attenuation in rain and fog. Experimental data show that in moderate rain and light fog, the effective range of the system is reduced by only 20-30%, while the traditional laser attenuates by 50-70%. In complex electromagnetic environments, the laser transmission does not depend on electromagnetic signals and is completely unaffected by interference from high-voltage lines, base stations, etc.

[0035] The system control and feedback module is used for fully automated control of the entire process, real-time monitoring of the strike effect and adjustment of parameters. The response time of the entire process is ≤6 seconds; the accuracy of strike effect assessment is ≥90%; and it supports automatic / manual dual-mode control.

[0036] To enable practical deployment, the system's engineering design focused on miniaturization, heat dissipation, and reliability, specifically as follows: (1) Miniaturized design: Through the integration of core components (the size of the optical core component is only 0.8 meters) and the application of lightweight materials (the main structure is made of aerospace aluminum alloy), the weight of the vehicle version is controlled within 500 kg, and the fixed deployment version can be integrated into a standard container with a deployment time of ≤2 hours; (2) High-efficiency heat dissipation system: The system adopts a combination of low-temperature cooling system and forced air cooling. The low-temperature cooling system precisely controls the temperature of the core components of the laser, while the forced air cooling system is responsible for the heat dissipation of the whole machine, ensuring that the system can work continuously for ≥24 hours in an environment of -20℃ to 50℃. (3) High reliability assurance: core components (femtosecond laser, fast reflector, etc.) are independently developed, the overall localization rate of parts exceeds 95%, and the localization rate of key components exceeds 80%, avoiding risks; the system has a built-in fault self-diagnosis module, which can monitor the status of each component in real time, the fault alarm response time is ≤1 second, and the mean time between failures (MTBF) is ≥2000 hours.

[0037] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A counter-drone method based on femtosecond laser multi-mechanism coordinated destruction, characterized in that, The specific steps are as follows: Multiple sensors work together to acquire the characteristics and trajectory parameters of low-altitude moving targets, and the AI ​​model completes classification and threat level determination based on the target parameter information. The target trajectory parameters are specifically optimized and the motion trend is predicted. The reflector, together with the beam pointing control system, focuses the laser spot onto the target surface according to the prediction results. Perform adaptive optics compensation by adjusting the laser emission parameters based on the target parameter information; The femtosecond laser emits laser light according to set parameters. After being focused by the aiming system, the beam acts on the target and destroys it. The time of impact is adjusted according to the target material. The damage result is determined based on the changes in the target's spot position and trajectory data, and a decision is made on whether to execute a secondary strike based on the damage determination.

2. The anti-drone method according to claim 1, characterized in that: In the multi-sensor collaborative operation, millimeter-wave radar is used for initial long-range detection, infrared thermal imager is used to capture target thermal features, visible light camera is used to collect detailed features, and lidar is used to obtain three-dimensional coordinates.

3. The anti-drone method according to claim 1, characterized in that: The target features and trajectory parameters are input into the hierarchical classification model, the classification result is output based on the target feature parameters, and the threat parameters are quantified based on the target trajectory parameters. The classification results and threat parameters are input into a dynamic decision tree model, which outputs the threat level and generates response instructions.

4. The anti-drone method according to claim 1, characterized in that: The adaptive optics compensation includes using a wavefront sensor to detect wavefront distortion caused by atmospheric turbulence, and a deformable mirror dynamically adjusting the laser wavefront based on the distortion data to compensate for laser beam diffusion. It also includes using self-focusing pulses excited by femtosecond lasers, combined with pulse amplification technology to suppress nonlinear effects, in order to reduce rain and fog scattering attenuation.

5. The anti-drone method according to claim 1, characterized in that: The specific process of emitting the laser is as follows: The optical fiber seed source generates the basic pulse with the following parameters: wavelength 1560nm, pulse width 100fs, repetition frequency 60MHz, average power 10mW, and single pulse energy 0.17nJ. By introducing negative dispersion through grating pairs or prism pairs, fiber stretchers can stretch the pulse width from 100 fs to 500 ps. First, the signal is amplified by an optical fiber amplifier, and then a low-temperature cooled solid-state amplifier is used to improve efficiency, ultimately outputting a pulse with a single pulse energy of 30mJ and a repetition frequency of 1kHz. By using a vacuum grating to compensate for positive dispersion and a nonlinear compressor to compress the pulse width to within 300 fs, the peak power reaches over 100 GW. The optical path offset is corrected using a fast-reflecting mirror, the pulse envelope is adjusted using an acousto-optic modulator, the spectral phase is optimized using a spatial light modulator, and the optical components are adjusted to M... 2 ≤1.3, final output peak power ≥100GW, M 2 Femtosecond lasers with a diameter of ≤1.3 ms.

6. The anti-drone method according to claim 1, characterized in that: The specific process of destroying the target is as follows: The laser is focused on the surface of the drone target and creates an ultra-strong electric field in an extremely small area, which strips the outer electrons of the material atoms and forms plasma. The plasma expands violently in a short period of time, forming a strong electromagnetic pulse with a wide frequency and high peak value, which then acts on the electronic components inside the drone. Ultrashort pulses inject energy into the target in an extremely short time, creating tiny craters only at the focal point.

7. The anti-drone method according to claim 1, characterized in that: The specific process for determining damage is as follows: Monitor the surface condition of the target, detect structural anomalies based on the YOLOv7 model, and simultaneously overlay infrared auxiliary data to annotate the laser ablation area; By analyzing point cloud data to determine the structural integrity and spatial position changes of the target, and comparing the differences in point cloud density before and after damage, the trajectory of the fallen drone is updated. If the damage threshold is not reached, the system automatically adjusts the aiming point and laser parameters for a second strike. If the target has been damaged, the system returns to standby mode to wait for the next target, supporting continuous interception of multiple targets.

8. An anti-drone system based on femtosecond laser multi-mechanism coordinated destruction, used to execute the anti-drone method according to any one of claims 1-7, characterized in that, The anti-drone system includes: The target detection and identification module is used to enable long-range detection, classification, identification, and threat level determination of low-altitude UAVs; A high-precision dynamic aiming module is used to track moving targets and precisely focus the laser onto the target surface; An ultrafast laser emission module is used to generate and directionally emit femtosecond laser pulses with high repetition rate and high peak power. An adaptive optics compensation module is used to counteract the effects of environmental factors such as atmospheric turbulence, rain, and fog on laser transmission. It integrates atmospheric turbulence compensation technology, which uses a wavefront sensor to detect wavefront distortion in real time during laser transmission and dynamically adjusts the laser wavefront through a deformable mirror to compensate for beam deflection caused by turbulence. It utilizes the self-focusing pulse effect, which uses the high energy of an ultrafast laser to excite the air lens effect, so that the laser forms a self-focusing pulse during transmission, reducing scattering attenuation in rain and fog. The system control and feedback module is used for fully automated control of the entire process, real-time monitoring of the strike effect and adjustment of parameters.

9. The anti-drone system based on femtosecond laser multi-mechanism coordinated destruction according to claim 8, characterized in that: The target detection and recognition module integrates multi-sensor fusion detection and AI visual compensation technology. The multi-sensor fusion detection integrates millimeter-wave radar, infrared thermal imager, visible light camera and lidar. AI visual compensation technology employs an adaptive tracking algorithm to specifically optimize the typical motion trajectory of drones and predict the target's motion trend in real time.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the anti-drone method as claimed in any one of claims 1 to 7.

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