Low-altitude anti-unmanned aerial vehicle interception control method based on electromagnetic bullet screen steel ball dense array

Abstract

The invention discloses a low-altitude anti-unmanned aerial vehicle interception control method based on an electromagnetic bullet screen steel ball dense array, and the method comprises the following steps: S1, constructing a millimeter-wave radar-infrared photoelectric composite coordinate system conversion model, and achieving the target positioning error compensation through Kalman filtering; s2, constructing a three-stage interception layer, and optimizing ballistic distribution through a honeycomb ballistic distribution algorithm; s3, constructing a nonlinear constraint optimization model to optimize the distribution density of the steel balls, and realizing microsecond parameter reconfiguration through an FPGA hardware accelerator; s4, forming an anti-cluster saturation attack strategy by adopting an asymmetric space-time coding technology; and S5, establishing a damage performance evaluation closed loop. According to the method, the interception success rate is remarkably increased, the launching cost is reduced, and the complex environment adaptability is improved.

Description

Technical Field

[0001] This invention belongs to the field of drone interception technology, and in particular, relates to a low-altitude anti-drone interception control method based on an electromagnetic barrage steel ball dense array. Background Technology

[0002] With the decreasing cost of drone development and manufacturing, the drone industry has grown rapidly and is applied to multiple sectors. However, this has also brought about issues related to drone safety management. Incidents of drones flying illegally into sensitive airspace and interfering with civil aviation are on the rise, causing serious negative impacts. These drones are low-cost, readily available, easy to operate, and numerous, making them difficult to regulate.

[0003] Existing technologies for multi-sensor data fusion suffer from latency and errors. For example, radar in urban environments is severely affected by ground clutter, and optoelectronic devices malfunction in low visibility conditions, leading to deviations in the timing and location of projectile launches. Furthermore, the lack of real-time data calibration mechanisms (such as the fusion of millimeter-wave radar and laser ranging) makes it difficult to cope with the rapid maneuverability of swarm drones. In urban anti-drone interception, the dense electromagnetic spectrum in cities (such as 5G base stations and Wi-Fi signals) may interfere with the power control and signal transmission of electromagnetic launch systems. Existing systems lack accurate identification capabilities, making it difficult to distinguish between legitimate drones (such as registered inspection drones) and unauthorized drones. For example, in airport airspace protection zones, if the system misjudges a normally taking off or landing drone as a threat and launches a projectile, it could cause an aviation safety accident. Electromagnetic launch devices require high-power power supplies and complex energy storage systems, resulting in high costs for a single unit. When deployed in a dense array configuration, the number of devices and maintenance costs increase exponentially.

[0004] On the other hand, when facing drone swarms, existing Phalanx systems mostly employ a single-target successive interception mode, lacking multi-target tracking and batch countermeasure algorithms. If 10 drones simultaneously intrude from different directions, the system may miss some due to insufficient computing power. In contrast, adaptive anti-drone systems can simultaneously interfere with more than 5 targets, while the response speed and parallel processing capabilities of kinetic interception urgently need improvement. Existing technologies generally suffer from weak environmental adaptability, high operating costs, and poor anti-swarming capabilities, making it difficult to meet the low-altitude protection requirements of densely populated urban areas and complex battlefield environments.

[0005] A patent with publication number CN117739746A discloses a kinetic interception system and method for countering low-speed, small unmanned aerial vehicles (UAVs), belonging to the field of anti-UAV technology. It includes a ground-based system and an interceptor UAV system. The ground-based system comprises an integrated command and control system and an active detection system. The integrated command and control system serves as the command and control center of the entire kinetic interception system, while the active detection system serves as the detection means. This invention is based on a novel anti-UAV mode where a self-intercepting UAV performs kinetic interception against low-speed, small UAVs. Through key technologies such as active detection technology for low-speed, small targets, UAV interception platform technology, and airborne information fusion and composite guidance technology, it achieves countermeasures against low-speed, small UAVs. The patent mentions that the active detection system includes a small multi-faceted radar and photoelectric detection equipment, but it does not explicitly describe its anti-interference capability in complex environments. Although it possesses swarm-to-swarm interception capability, it does not detail the collaborative strategies between multiple interceptor UAVs. Summary of the Invention

[0006] This invention addresses the shortcomings of existing technologies, such as weak environmental adaptability, high operating costs, and poor anti-swarming capabilities, which make it difficult to meet the low-altitude protection needs of densely populated urban areas and complex battlefield environments. It proposes a low-altitude anti-drone interception and control method based on an electromagnetic barrage steel ball dense array.

[0007] A low-altitude anti-UAV interception and control method based on an electromagnetic barrage steel ball dense array includes the following steps: S1. Multi-source sensing data fusion: Construct a millimeter-wave radar-infrared optoelectronic composite coordinate system transformation model, and use Kalman filtering to achieve target positioning error compensation; then use the LSTM+Transformer hybrid architecture to deploy a deep learning trajectory prediction network, input the historical trajectory point sequence and output the probability cloud map of the motion state in the next 3 seconds; S2. Honeycomb-style ballistic layered control: A three-level interception layer is constructed based on spatial fractal theory. The three-level interception layer includes a leading layer, a main interception layer, and a blind-filling layer. The ballistic distribution is optimized through a honeycomb-style ballistic distribution algorithm. S3. Dynamic density field control: Construct a nonlinear constraint optimization model to optimize the steel ball distribution density, and realize microsecond-level parameter reconfiguration through FPGA hardware accelerator; S4. Anti-cluster saturation attack: The anti-cluster saturation attack strategy is composed of time-domain coding with pseudo-random pulse interval modulation and spatial three-dimensional wavefront shaping technology based on Zernike polynomials. S5. Execution and Feedback: Combine millimeter-wave holographic imaging with real-time feedback to realize the bullet screen pattern and autonomously reconstruct the bullet screen pattern.

[0008] Furthermore, in step S1, the sampling frequency of the fused data of the millimeter-wave radar and the infrared photoelectric sensor is 100Hz, and the target position and velocity are calibrated in real time through Kalman filtering. After error compensation, the positioning accuracy is ≤0.1m.

[0009] Furthermore, in step S2, the trajectory of the steel ball is calculated in real time by solving the six-degree-of-freedom ballistic differential equation, and the trajectory is dynamically corrected by combining environmental parameters such as wind speed and air density. The expressions are as follows:

[0010]

[0011]

[0012]

[0013] In the above formula, Let be the component of air resistance in the coordinate system. air density, Let be the velocity component of the steel ball in the ground coordinate system. Let be the component of wind speed in the ground coordinate system. This is the speed of the steel ball relative to the air. This is the corrected drag coefficient. This is the reference area of ​​the steel ball. For torque, This is the torque coefficient. The diameter of the steel ball; Among them, based on Reynolds number and turbulence intensity Regarding air drag coefficient Correction, corrected air drag coefficient The expression is:

[0014] In the above formula, The magnitude of the relative velocity. Reynolds number, turbulence intensity , This is the basic drag coefficient. Aerodynamic viscosity, This is the turbulence correction factor.

[0015] Furthermore, in step S2, the leading layer is located in an area 50-100m away from the anti-drone system, and the leading layer is distributed in a honeycomb hexagonal pattern with a density of 1200 beads / m². 3The main interception layer is located in the area 20-50m away from the anti-drone system. The main interception layer adopts a dynamically shrinking honeycomb structure with an adaptive density adjustment range of 800-1500 beads / m². 3 The blind spot layer is the area within 20m of the anti-drone system. The blind spot layer adopts an omnidirectional shotgun mode with a trigger firing rate of 5000 bullets / 0.1s.

[0016] Furthermore, in step S2, the honeycomb ballistic distribution algorithm includes: establishing a three-dimensional coordinate system for the interception airspace and dividing it into 1m×1m×1m grid cells; then calculating the optimal detonation sequence of each launch unit based on the target velocity vector field; and finally introducing Monte Carlo simulation to calibrate the spatial distribution of steel balls so that the density deviation within the confidence interval is ≤5%.

[0017] Furthermore, in step S3, the nonlinear constraint optimization model aims to minimize the variance between the actual density and the target density of the steel ball. The constraints include a continuity equation, a minimum density requirement within the interception volume, and hardware control boundary constraints. The objective function expression of the nonlinear constraint optimization model is as follows:

[0018] The constraints include: (Continuity equation); (Minimum density requirement); (Hardware control boundaries); Wherein, the control vector represents the adjustable parameters of N electromagnetic launching units, such as pulse current amplitude, phase, and detonation timing; This represents the ideal target density distribution set based on target trajectory prediction and interception strategies; For the entire interception airspace; This is a divergence operator used to describe the conservation of mass; For the position of the steel ball The velocity vector field at that location is determined by both the six-degree-of-freedom ballistic model and the ambient wind field. The key interception sub-region defined by tactics; The minimum effective steel ball density threshold required for the interception mission; Indicates in control parameters Below, the steel ball is in position The actual volume density at that location; Indicates the first Physical control limits for each transmitter unit (e.g., maximum current 5 kA, minimum trigger interval 10 µs).

[0019] Furthermore, the parameter reconfiguration of the FPGA hardware accelerator includes pulse power supply timing jitter of less than 10ns, coil array phase synchronization error of less than 0.01°, and Doppler compensation real-time loading delay of less than 50µs.

[0020] Furthermore, in step S4, the time-domain encoding uses a pseudo-random pulse repetition frequency adjustable from 1 to 10 kHz, and the spatial-domain encoding generates a three-dimensional wavefront-shaped barrage using Zernike polynomials for asymmetric spatiotemporal coverage of the UAV swarm.

[0021] Furthermore, it also includes step S6, damage performance assessment closed loop: real-time feedback of the interception gap through millimeter-wave holographic imaging, automatically switching between ring, cone or spherical barrage patterns, with barrage switching time less than 80 milliseconds.

[0022] A low-altitude anti-drone interception system includes an electromagnetic launch unit, a barrage generation controller, and an intelligent ammunition supply device, employing the aforementioned low-altitude anti-drone interception method.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention, through a honeycomb ballistic distribution algorithm combined with Monte Carlo simulation calibration, achieves a bullet density deviation of ≤5%, forming a uniform interception network of 1200 beads / m³ within a range of 50-100m. This increases the coverage area for UAV swarms to 3.2 times that of traditional shotgun blasting, and raises the single-shot interception hit rate from 42.1% to 97.3%. The dynamically shrinking main interception layer effectively counters UAVs' emergency trajectory changes by adjusting the honeycomb density (800-1500 beads / m³) in real time, increasing the interception success rate against high-speed targets to 89.5%. The blind-spot layer uses trigger-type omnidirectional shotgun shells, forming a steel ball kinetic energy barrier in extremely short-range airspace, achieving 100% interception of residual targets that have penetrated the first two layers, significantly improving the interception success rate of the anti-UAV system.

[0024] 2. This invention uses non-metallic ceramic beads (zirconia) with a single-shot cost of only US$0.12, which is 99.92% lower than that of traditional missiles ($15 / shot), and the reusability rate reaches 35%. The electromagnetic acceleration array uses multi-stage coil coordinated drive, with an energy conversion efficiency of 68%, compared to only 12% for traditional gunpowder launches, thus reducing the cost per shot.

[0025] 3. This invention employs asymmetric spatiotemporal coding technology, combining pseudo-random pulse interval modulation with Zernike polynomial wavefront shaping and real-time feedback from millimeter-wave holographic imaging. It achieves a 91.7% interception success rate against 200 saturation attacks, far exceeding traditional methods, demonstrating strong resistance to saturation cluster attacks. Attached Figure Description

[0026] Figure 1This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the anti-drone interception system of the present invention.

[0027] In the image above, 1. Electromagnetic launch unit; 2. Barrage generation controller; 3. Intelligent ammunition supply device. Detailed Implementation

[0028] To clearly illustrate the technical features of the present invention, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the invention; however, the invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below. In the present invention, unless otherwise expressly specified and limited, the first feature "on" or "below" the second feature may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that the specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0029] Example 1 like Figure 1 As shown, a low-altitude anti-UAV interception and control method based on an electromagnetic barrage steel ball phallic array includes the following steps: S1. Multi-source sensing data fusion: Construct a millimeter-wave radar-infrared optoelectronic composite coordinate system transformation model, and achieve target positioning error compensation through Kalman filtering; Deploy a deep learning trajectory prediction network using an LSTM+Transformer hybrid architecture, input historical trajectory point sequences and output a probability cloud map of the motion state in the next 3 seconds; S2. Honeycomb-style ballistic layered control: A three-level interception layer is constructed based on spatial fractal theory. The three-level interception layer includes a leading layer, a main interception layer, and a blind-filling layer. The ballistic distribution is optimized through a honeycomb-style ballistic distribution algorithm. S3. Dynamic density field control: Construct a nonlinear constraint optimization model to optimize the steel ball distribution density, and realize microsecond-level parameter reconfiguration through FPGA hardware accelerator; S4. Anti-cluster saturation attack: The anti-cluster saturation attack strategy is composed of time-domain coding with pseudo-random pulse interval modulation and spatial three-dimensional wavefront shaping technology based on Zernike polynomials. S5. Execution and Feedback: Combine millimeter-wave holographic imaging with real-time feedback to realize the bullet screen pattern and autonomously reconstruct the bullet screen pattern.

[0030] This embodiment is applicable to low-altitude protection around airport runways and aprons, and is used to deal with illegal intrusion, terrorist attacks, or aerial reconnaissance by small and medium-sized drones.

[0031] In this embodiment, the hardware configuration includes a millimeter-wave radar (100Hz update rate, 0.1m resolution) and an infrared thermal imager (640×512 pixels, temperature measurement range -20~150℃) integrated on the top of the electromagnetic emission unit 1 to construct a composite sensing array.

[0032] Specifically, the control method steps include (1) Multi-source data fusion and trajectory prediction: The radar collects the UAV position in real time, the infrared thermal imager identifies the target outline through temperature characteristics, and outputs the three-dimensional coordinates of the target after Kalman filtering fusion, with error compensation ≤0.05m. The LSTM+Transformer network is used to input the target trajectory of the past 10 seconds, the sampling frequency is set to 100Hz, and the motion probability cloud map of the next 3 seconds is predicted to determine the target flight path. (2) Honeycomb ballistic layered control: Based on the spatial fractal theory, a three-level interception layer is constructed, in which the lead-in layer (50~100m) launches ceramic beads in a honeycomb hexagonal grid, the side length of the honeycomb hexagonal grid is 0.5m, and the density is 1200 beads / m. 3 This forms a pre-interception barrier; the main interception layer (20-50m) adopts a dynamically shrinking honeycomb structure with a shrinkage rate of 0-5m / s. When the drone speed is ≥30m / s, the honeycomb structure dynamically shrinks according to the drone speed, increasing the density to 1500 beads / m². 3 The system corrects for wind resistance in real time using a six-degree-of-freedom ballistic equation; the blind spot layer (<20m) uses a triggered omnidirectional shotgun mode, launching 5000 balls within 0.1 seconds to form a steel ball barrage with a diameter of 10m; the ballistic distribution is then optimized using a honeycomb ballistic distribution algorithm, which includes: establishing a three-dimensional coordinate system for the interception airspace and dividing it into 1m×1m×1m grid cells; calculating the optimal detonation sequence for each launching unit based on the target velocity vector field; and finally, introducing Monte Carlo simulation to calibrate the spatial distribution of the steel balls, ensuring that the density deviation within the confidence interval is ≤5%.

[0033] Real-time solution includes calculation of translational motion equations: differentiating the position coordinates of the steel ball in the ground coordinate system yields... The differential of the steel ball velocity is obtained ; Force Including gravity and aerodynamics, gravity Aerodynamics .in, For air resistance, This is the Magnus force (if the steel ball is rotating). Assume the Magnus force generated by the rotating steel ball is small and can be ignored. .

[0034] Air resistance corrected by turbulence correction model The expression is:

[0035]

[0036]

[0037] In the above formula, This represents the position coordinates of the steel ball in the ground coordinate system. Let be the component of air resistance in the coordinate system. air density, Let be the velocity component of the steel ball in the ground coordinate system. Let be the component of wind speed in the ground coordinate system. This is the speed of the steel ball relative to the air. This is the corrected drag coefficient. This is the reference area of ​​the steel ball.

[0038] The equations of motion for rotation are calculated as follows: the attitude of the steel ball is expressed using Euler angles. (Rolling angle) (Pitch angle) (Yaw angle) description, angular velocity in the projectile coordinate system is Since the steel ball is a sphere, the inertial tensor... It is a scalar, that is ; The differential of angular velocity (in projectile coordinates) is obtained Among them, torque It is mainly composed of aerodynamic torque. This represents the torque component acting on the steel ball (in projectile coordinates). For a sphere, the aerodynamic torque is usually proportional to the angular velocity and can be expressed as:

[0039] in, For torque, This is the torque coefficient. The diameter of the steel ball; Based on Reynolds number and turbulence intensity Regarding air drag coefficient Corrections were made to account for the effect of turbulence on drag in low-altitude environments; the corrected air drag coefficient is... The expression is:

[0040] In the above formula, The magnitude of the relative velocity. The Reynolds number is... Aerodynamic viscosity, Here are the turbulence correction factors, and their values ​​are respectively: .

[0041] The basic drag coefficient (for a smooth sphere under laminar flow conditions) is applicable to... The expression is:

[0042] Turbulence intensity is , This represents the average wind speed. This represents the standard deviation of wind speed fluctuation.

[0043] Dynamic density field control: Constraints include the continuity equation, minimum density requirement within the interception volume, and hardware control boundary constraints. A nonlinear constraint optimization model is constructed, and the objective function expression is as follows:

[0044] The constraints include: (Continuity equation); (Minimum density requirement); (Hardware control boundaries); Here, the control vector represents the adjustable parameters of N electromagnetic launching units, such as pulse current amplitude, phase, and detonation timing. This represents the ideal target density distribution set based on target trajectory prediction and interception strategies; For the entire interception airspace; This is a divergence operator used to describe the conservation of mass; For the position of the steel ball The velocity vector field at that location is determined by both the six-degree-of-freedom ballistic model and the ambient wind field. The key interception sub-region defined by tactics; The minimum effective steel ball density threshold required for the interception mission; Indicates in control parameters Below, the steel ball is in position The actual volume density at that location; Indicates the first Physical control limits for each transmitter unit (e.g., maximum current 5 kA, minimum trigger interval 10 µs).

[0045] Then, the pulse power supply timing is optimized in real time using FPGA hardware accelerator (jitter <10ns) to ensure that the phase synchronization error of the coil array is <0.01 radians and the real-time loading delay of the Doppler compensation is less than 50µs, thus achieving microsecond-level parameter reconfiguration.

[0046] (4) Anti-saturation attack: The time domain uses a 5kHz pseudo-random pulse interval (PRF), and the air domain uses Zernike polynomial to generate a cone-shaped barrage to cover the core area of ​​the UAV formation.

[0047] This invention achieves a radar detection time of 180ms from initial firing to interception, a 4.2-second interception window for targets within 200m, and a 100% interception rate for a linear formation of 5 aircraft, a 96.3% interception rate for a swarm of 50 aircraft, and a 91.7% interception rate for a saturation attack of 200 aircraft (based on actual testing with a prototype). This significantly improves the interception success rate and is adaptable to various extreme environments. Specifically, the counter-swarm attack test results are shown in Table 1, and the environmental test results are shown in Table 2.

[0048] Table 1. Tests against cluster attacks

[0049] Table 2 Environmental Test Results

[0050] Example 2 like Figure 1 As shown, a low-altitude anti-UAV interception and control method based on an electromagnetic barrage steel ball phallic array includes the following steps: S1. Multi-source sensing data fusion: Construct a millimeter-wave radar-infrared optoelectronic composite coordinate system transformation model, and achieve target positioning error compensation through Kalman filtering; Deploy a deep learning trajectory prediction network using an LSTM+Transformer hybrid architecture, input historical trajectory point sequences and output a probability cloud map of the motion state in the next 3 seconds; S2. Honeycomb-style ballistic layered control: A three-level interception layer is constructed based on spatial fractal theory. The three-level interception layer includes a leading layer, a main interception layer, and a blind-filling layer. The ballistic distribution is optimized through a honeycomb-style ballistic distribution algorithm. S3. Dynamic density field control: Construct a nonlinear constraint optimization model to optimize the steel ball distribution density, and realize microsecond-level parameter reconfiguration through FPGA hardware accelerator; S4. Anti-cluster saturation attack: The anti-cluster saturation attack strategy is composed of time-domain coding with pseudo-random pulse interval modulation and spatial three-dimensional wavefront shaping technology based on Zernike polynomials. S5. Execution and Feedback: Combine millimeter-wave holographic imaging with real-time feedback to realize the bullet screen pattern and autonomously reconstruct the bullet screen pattern.

[0051] This embodiment is applicable to low-altitude protection around airport runways and aprons, and is used to deal with illegal intrusion, terrorist attacks, or aerial reconnaissance by small and medium-sized drones.

[0052] In this embodiment, the hardware configuration includes a millimeter-wave radar (100Hz update rate, 0.1m resolution) and an infrared thermal imager (640×512 pixels, temperature measurement range -20~150℃) integrated on the top of the electromagnetic emission unit to construct a composite sensing array.

[0053] This embodiment provides a method for counter-drone control at military bases. Specifically, 16 interception systems are arranged in a ring, covering a 500m airspace radius after networking. The electromagnetic launch unit 1 integrates a double-layer electromagnetic shield (inner layer µ-metal alloy + outer layer carbon fiber), with a shielding effectiveness of 60dB@1GHz (compliant with GJB151B military standard). The intelligent ammunition supply device 3 includes a main ammunition magazine of 100,000 rounds and an emergency ammunition magazine of 50,000 rounds, supporting continuous firing at 2,000 rounds / minute and a full-load combat time of 8 minutes. The main radar adopts a phased array system with a detection range of 1km. In conjunction with a distributed infrared sensor network, 64 units are synchronously controlled via the TDMA protocol (communication latency <5ms).

[0054] In this embodiment, the drone interception and control method is as follows: (1) Anti-interference data fusion: Under strong electromagnetic interference of 30V / m, the interference is blocked by double-layer shielding. Kalman filtering still maintains positioning accuracy ≤0.2m. The LSTM+Transformer trajectory prediction network is continuously trained online. The prediction error decreases over time, and the trajectory prediction network error is 0.18m@3s.

[0055] (2) Saturation attack response: A 10kHz high-frequency pulse (PRF adjustable) is used in the time domain, and a spherical barrage (Zernike polynomial order n=5) is generated in the air domain, covering a 360° azimuth angle with a density of 1500 beads / m³ (leader layer + main layer superposition). The interception gap is intercepted in real time through millimeter-wave holographic imaging, and it switches to a ring-shaped barrage within 80ms (targeting the area where the leaky UAVs gather).

[0056] (3) Damage effectiveness assessment closed loop: Damage assessment is completed within 0.5 seconds after each shot, and the subsequent launch elevation angle (15°~85°) and barrage density are adjusted autonomously to ensure an interception rate of ≥91.7% against saturation attacks of 200 aircraft.

[0057] In strong electromagnetic environments, the system response delay increases by only 22ms, with an operational stability of 99.7%, far exceeding traditional electronic jamming systems. With a network of 64 units, the system can defend an airspace of 10km², achieving a target tracking capacity of 500 targets per second, meeting the requirements of large-scale cluster operations.

[0058] Example 3 like Figure 2 As shown, a low-altitude anti-drone interception system includes an electromagnetic launch unit 1, a bullet generation controller 2, and an intelligent ammunition supply device 3. The electromagnetic launch unit 1 adopts a compact modular design, including a launching electromagnetic coil, a drive circuit, an electric drive, and a launch tube. The electromagnetic acceleration array uses a 10-stage coil, with a single-stage acceleration efficiency exceeding 90%, ensuring the efficient delivery of zirconia ceramic beads (Φ6mm, density 6g / cm³). 3 The system accelerates to a speed sufficient for interception. The barrage generation controller 2 integrates a ballistic calculation chip and a phased array radar data link to calculate the drone swarm's trajectory in real time and dynamically adjust the launch elevation and bullet dispersion density. The intelligent ammunition supply device 3 employs a gravity-driven, redundant design, equipped with a main ammunition magazine and an emergency ammunition magazine. The main magazine has a capacity of 50,000 rounds, and the emergency ammunition magazine has a capacity of 10,000 rounds, ensuring uninterrupted ammunition supply during prolonged combat. The ammunition supply system works closely with the electromagnetic launch unit and can automatically adjust the ammunition supply rate according to launch requirements.

[0059] This embodiment provides a control method for a covertly deployed drone interception system in urban areas. Specifically, the interception control method includes the following steps: (1) Multi-source data fusion: Data is collected in real time by millimeter-wave radar and infrared camera. The data fusion algorithm operates according to the following process. First, the target position and velocity information obtained by millimeter-wave radar is transformed into a unified coordinate system. The Kalman filter algorithm is used to predict and correct the target position. Combined with the temperature information provided by the infrared camera, the target is further accurately located to compensate for the positioning error, so that the final target positioning accuracy reaches ≤0.1m. Then, the fused target data is input into the deep learning trajectory prediction network with LSTM+Transformer hybrid architecture. The network receives the historical trajectory point sequence of the target at a sampling rate of 100Hz. After training, it outputs the motion state probability cloud map for the next 3 seconds, providing an accurate basis for subsequent interception decisions.

[0060] (2) Barrage Dispersion Control: Based on the target's distance and motion state, a honeycomb-style ballistic layered control strategy is activated. When the target is within the 50-100m lead layer, ceramic beads are launched in a honeycomb hexagonal dispersion pattern, with a barrage density set at 1200 beads / m³, forming a preliminary interception barrier. When the target enters the 20-50m main interception layer, the density is automatically adjusted by a dynamically shrinking honeycomb structure based on the target's speed and real-time position. If the target's speed is high, it may cause it to quickly pass through the interception area; in this case, the density is increased to 1500 beads / m³. 3 If the target speed is slow, the density should be appropriately reduced to 800 beads / m³. 3While ensuring interception effectiveness, it avoids unnecessary ammunition waste. When the target approaches the blind spot layer of less than 20m, the omnidirectional shotgun mode is triggered, firing 5000 balls within 0.1s to form an all-round steel ball kinetic energy barrier, ensuring effective interception of any remaining targets that have broken through the first two layers.

[0061] (3) Dynamic density field control: Based on the real-time target distribution and motion, the system constructs a nonlinear constraint optimization model to adjust the dispersion density of the steel balls. With the goal of minimizing the variance between the actual steel ball density and the target density, microsecond-level parameter reconfiguration is achieved through an FPGA hardware accelerator. Specifically, the pulse power supply timing jitter is controlled within <10ns to ensure stable output of the emitted energy; the coil array phase synchronization error is controlled within <0.01 radians to ensure consistency of electromagnetic acceleration; and the real-time loading delay of the Doppler compensation is controlled within <50μs to improve the tracking accuracy of moving targets. Through the precise control of these parameters, a uniform distribution of the steel balls in the interception airspace is achieved, ensuring density deviation within a 95% confidence interval.

[0062] (4) Anti-swarm saturation attack strategy: In urban environments, there may be threats from drone swarms. To address this, the system employs asymmetric spatiotemporal coding technology. In the time domain, pseudo-random pulse interval modulation is used, with the pulse repetition frequency adjustable within the range of 1 to 10 kHz. By randomizing the pulse emission interval, the detection difficulty of the drone defense system is increased, reducing its ability to predict our emission patterns. In the air domain, three-dimensional wavefront shaping is performed based on Zernike polynomials, enabling the launched steel ball barrage to more effectively cover the drone swarm in space, improving interception efficiency. Simultaneously, millimeter-wave holographic imaging technology is used to provide real-time feedback on interception gaps. Once a drone is detected as not being successfully intercepted, the system autonomously reconstructs the barrage shape within 80 ms, switching from the default barrage shape to a more suitable shape such as a ring, cone, or spherical barrage, ensuring continuous and effective interception of swarm targets.

[0063] (5) Bullet Screen Density Optimization Algorithm: Considering the impact of changes in wind speed and drone swarm size on the interception effect in the urban environment, an urban environment bullet screen density optimization algorithm (optimized via MATLAB) is adopted to adjust the bullet screen density in real time. This algorithm calculates the final bullet screen density based on the input number of drones and wind speed data, combined with a preset base density value, using two factors: wind resistance correction and swarm size compensation. The specific formula is:

[0064] In the above formula, This indicates the final output density of the steel ball barrage, used in the main interception layer or the leading layer; This represents the base bullet density (preset value in a windless, single-target scenario), taken as 1200 bullets / m. 3; Indicates the current wind speed. The wind speed reference value is 10 meters per second; in this embodiment... This represents the drag correction factor, reflecting the degree of influence of wind speed on the dispersion of bullets, ranging from 0.2 to 0.4; This indicates the number of drones that need to be intercepted simultaneously within the current threat airspace. This represents the cluster size compensation coefficient, reflecting the density redundancy required to increase the target number, ranging from 0.15 to 0.3. This represents a logarithmic cluster compensation term, which avoids the linear explosive growth of density with quantity and reflects diminishing marginal benefits.

[0065] Based on the calculation results, the system automatically adjusts the launch parameters to ensure optimal interception performance under different urban environmental conditions.

[0066] Operational Effectiveness Evaluation: Tests were conducted in a simulated urban environment with drone swarms of varying sizes and flight trajectories. When facing a small swarm of 10 drones, the system achieved an interception success rate exceeding 98%. Even against a swarm attack of 30 drones, the success rate remained around 95%. The system's response time from target detection to the firing of the first munition was 180ms, ensuring timely interception of intruding drones. Throughout the operation, thanks to precise control algorithms and efficient interception methods, the probability of collateral damage was kept extremely low, minimizing the impact on the surrounding environment and personnel. Furthermore, analysis of multiple test data verified that the system operates stably and efficiently under complex urban environmental conditions, including varying wind speeds and drone swarm sizes, effectively ensuring low-altitude security in key urban areas.

[0067] Obviously, the embodiments described above are merely examples for clearly illustrating the present invention and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A low-altitude anti-UAV interception control method based on an electromagnetic barrage steel ball dense array, characterized in that, Comprise the following steps: S1, multi-source perception data fusion: construct a millimeter wave radar-infrared photoelectric composite coordinate system conversion model, realize target positioning error compensation through Kalman filtering; then deploy a deep learning trajectory prediction network using LSTM+Transformer hybrid architecture, input historical trajectory point sequence and output future 3s motion state probability cloud map; S2, honeycomb trajectory control: based on spatial fractal theory, a three-level interception layer is constructed, which includes a front guide layer, a main interception layer and a blind filling layer, and the trajectory distribution is optimized through a honeycomb trajectory distribution algorithm; S3, dynamic density field regulation: a nonlinear constraint optimization model is constructed to optimize the steel ball scattering density, and a microsecond-level parameter reconfiguration is realized through an FPGA hardware accelerator; S4, anti-cluster saturation attack: a time domain coding using pseudo-random pulse interval modulation and a three-dimensional wavefront shaping technology based on Zernike polynomial are used to form an anti-cluster saturation attack strategy; S5, execution and feedback: combine millimeter wave holographic imaging to realize real-time feedback and achieve the shape of the bullet screen and automatically reconstruct the shape of the bullet screen.

2. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, In step S1, the fusion data sampling frequency of the millimeter wave radar and the infrared photoelectric sensor is 100Hz, the target position and velocity are calibrated in real time through Kalman filtering, and the positioning accuracy after error compensation is ≤0.1m.

3. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, In step S2, the steel ball motion trajectory is calculated in real time by solving the six-degree-of-freedom trajectory differential equation, and the trajectory is dynamically corrected according to environmental parameters such as wind speed and air density, and the expressions are respectively: in the above formula, is the component of the air resistance in the coordinate system, is the air density, is the component of the velocity of the steel ball in the ground coordinate system, is the component of the wind speed in the ground coordinate system, is the velocity of the steel ball relative to the air, is the corrected drag coefficient, is the reference area of the steel ball, is the moment, is the moment coefficient, is the diameter of the steel ball; wherein the air resistance coefficient C is corrected based on the Reynolds number Re and the turbulence intensity Tu The expression of the corrected air resistance coefficient C is​​​ In the above formula, is the relative velocity magnitude, is the Reynolds number, the turbulence intensity , is the base drag coefficient, is the aerodynamic viscosity, is the turbulence correction coefficient.

4. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, In the step S2, the leading layer is a region 50-100 m away from the anti- drone system, the leading layer adopts a honeycomb hexagonal distribution, and the density is 1200 beads / m 3 ; the main interception layer is a region 20-50 m away from the anti-drone system, the main interception layer adopts a dynamic contraction honeycomb structure, and the adaptive adjustment range of the density is 800-1500 beads / m 3 ; the blind-filling layer is a region within 20 m from the anti-drone system, the blind-filling layer adopts an omnidirectional canister mode, and the trigger type excitation rate is 5000 beads / 0.1 s.

5. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, In step S2, the honeycomb trajectory distribution algorithm includes: establishing a three-dimensional coordinate system of the interception space, dividing a 1m×1m×1m grid unit; then calculating the optimal ignition timing of each launch unit according to the target velocity vector field; finally, introduce Monte Carlo simulation to calibrate the spatial distribution of steel balls, so that the density deviation in the confidence interval is ≤5%.

6. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, In step S3, the nonlinear constraint optimization model takes minimizing the variance of the actual density and the target density of the steel balls as the target, and the constraint conditions include continuity equation and minimum density requirement in the interception volume, and the objective function expression of the nonlinear constraint optimization model is as follows: The constraint conditions include: ； ； ； where is the control vector representing adjustable parameters of N electromagnetic launchers; represents the ideal target density distribution set according to the target trajectory prediction and interception strategy; is the whole interception airspace; is the divergence operator; is the velocity vector field of steel balls at position , which is determined by the six-degree-of-freedom trajectory model and the environmental wind field; is the key interception sub-area defined by tactics; is the minimum effective steel ball density threshold required by the interception task; represents the actual volume density of steel balls at position under the control parameters ; represents the physical control limit of the th launcher.

7. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, The parameter reconfiguration of the FPGA hardware accelerator includes that the pulse power timing jitter is less than 10ns, the coil array phase synchronization error is less than 0.01°, and the Doppler compensation amount real-time loading delay is less than 50µs.

8. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, In step S4, the time domain coding uses a pseudo-random pulse repetition frequency of 1~10kHz, and the spatial domain coding generates a three-dimensional wavefront shaping bullet screen through a Zernike polynomial, which is used for asymmetric space-time coverage of the UAV cluster.

9. The low-altitude anti-UAV interception control method based on the electromagnetic barrage steel ball dense array according to claim 1, characterized in that, It also includes step S6, damage effectiveness evaluation closed loop: through millimeter wave holographic imaging, the interception gap is fed back in real time, and the bullet screen shape is automatically switched to ring, cone or sphere, and the bullet screen switching time is less than 80ms.

10. A low-altitude anti-UAV interception system based on an electromagnetic barrage steel ball dense array, comprising an electromagnetic launching unit, a barrage generation controller and an intelligent ammunition supply device, characterized in that, The low-altitude anti-UAV interception method of any one of claims 1~9 is adopted.

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