An unmanned aerial vehicle precision countermeasure system based on RIS metasurface

Abstract

This invention relates to the field of drone countermeasures, specifically disclosing a precise drone countermeasure system based on a RIS metasurface. The system includes: a signal sensing module responsible for transmitting captured drone signals to a dual-FPGA collaborative control module; the dual-FPGA collaborative control module responsible for sending an interference strategy generated based on the drone signals to the RIS metasurface module; and the RIS metasurface module responsible for generating and transmitting a directional countermeasure signal based on the interference strategy. A feedback module monitors the countermeasure effect of the RIS metasurface module and feeds back the monitoring data to the dual-FPGA collaborative control module to adjust the interference strategy and phase control commands. A power supply module provides power to the system. This invention addresses the problems of inaccurate interference, reliance on active transmission, and high countermeasure costs associated with existing technologies.

Description

Technical Field

[0001] This application relates to the field of drone countermeasures, specifically to a precision drone countermeasure system based on RIS metasurfaces. Background Technology

[0002] The frequent occurrence of drones flying illegally and haphazardly has transformed low-altitude safety threats from potential risks into real challenges.

[0003] Some solutions utilize multi-source sensor fusion and AI decision-making systems, such as employing radio frequency sensors and infrared cameras working together to detect and counter drones. The data collected by both is fused and processed using AI algorithms to identify the drone type and assess its threat level. However, these methods rely on external sensor networks, lack directional signal control capabilities, and allow interference signals to easily spread to non-target areas, potentially interfering with legitimate electronic devices in the vicinity.

[0004] Some solutions incorporate active radio frequency transmission modules to actively emit jamming signals to suppress drone communication, and use metasurfaces for beam enhancement to actively transmit signals; however, these solutions emit large amounts of electromagnetic radiation, are easy to expose themselves, and have high power consumption, which does not meet the requirements for passive covert countermeasures.

[0005] Other options involve using laser weapons, but the cost of a single strike is high, and the effective range of laser weapons is severely reduced in rainy or foggy weather conditions, making them unsuitable for low-cost, all-weather countermeasures. Summary of the Invention

[0006] In view of this, the present invention proposes a precision countermeasure system for unmanned aerial vehicles based on RIS metasurfaces, comprising: The signal sensing module is responsible for transmitting the captured drone signals to the dual FPGA collaborative control module. The dual FPGA collaborative control module is responsible for sending the interference strategy generated based on the UAV signal to the RIS metasurface module. The RIS metasurface module generates and transmits directional countermeasure signals based on the aforementioned interference strategy. The feedback module monitors the countermeasure effect of the RIS metasurface module and feeds the monitoring data back to the dual FPGA collaborative control module to adjust the interference strategy and phase control commands. The power module provides power to the system.

[0007] Furthermore, the signal sensing module includes a wideband radio frequency receiver and an analog-to-digital converter. The wideband radio frequency receiver captures UAV signals in real time, which are then converted into digital signals by the analog-to-digital converter and transmitted to the dual FPGA collaborative control module.

[0008] Furthermore, the signal sensing module also includes a miniature infrared sensor for enhancing the capture of drone signals in conjunction with the broadband radio frequency receiver at night or in low visibility conditions.

[0009] Furthermore, the dual FPGA collaborative control module includes a master FPGA and a slave FPGA; The main control FPGA receives the UAV signal from the signal sensing module, extracts its features, calculates the distance and signal stability of the UAV based on the extracted features, selects an interference strategy, and transmits the interference strategy to the slave control FPGA. The slave FPGA performs metasurface phase calculation and encoding based on the interference strategy, and optimizes the phase using an improved genetic algorithm to prevent the countermeasure signal from spreading to non-target areas.

[0010] Furthermore, the feature extraction extracted by the main control FPGA includes: It employs a parallel processing architecture to simultaneously extract signal frequency, modulation scheme, code rate, and signal strength.

[0011] Furthermore, the calculation of the UAV's distance and signal stability based on the extracted features includes: Based on the free space propagation loss model formula, the target distance is calculated in real time, and the calculation results are classified into short range, medium range and long range. Stability is determined according to preset criteria, and the results include high stability, medium stability, and low stability. The preset criteria include frequency stability, modulation consistency, signal strength fluctuation and code rate stability. If all preset criteria are met, the system is judged to be highly stable. If one or two criteria are not met, the system is judged to be moderately stable. If only one criterion is met, the system is judged to be lowly stable.

[0012] Furthermore, the interference strategy includes: Signal suppression jamming targets close-range, highly stable targets by directionally amplifying the drone's communication signals and reflecting them back to the drone. Signal command deception, which reflects the drone's communication signals back to the drone in reverse, is used against medium-range and moderately stable targets; Spectrum obfuscation involves sending false remote control feedback signals or false positioning commands to the drone, targeting distant and unstable targets.

[0013] Furthermore, the RIS metasurface module employs an array of distributed electromagnetic control units, each of which includes a driving circuit and a PIN diode. The driving circuit is used to receive the interference strategy of the dual FPGA collaborative control module and control the PIN diode to complete the generation and transmission of directional countermeasure signals.

[0014] Furthermore, after a certain period of time following the transmission of the countermeasure signal, the feedback module detects the signal strength of the drone. If the value drops to the threshold, the countermeasure is deemed successful, and a success signal is sent to the dual FPGA collaborative control module to reduce the strength of the interference strategy. If the value does not drop to the threshold, the countermeasure is deemed unsuccessful, and a failure signal is sent to the dual FPGA collaborative control module to regenerate the interference strategy until the countermeasure is successful.

[0015] Furthermore, the power module supports battery power.

[0016] This invention achieves precise acquisition and strategy generation of UAV signals through the cooperation of a signal sensing module and a dual FPGA collaborative control module. It utilizes a RIS metasurface module to perform directional signal modulation, enabling precise directional transmission of countermeasure signals and strict control over the interference range. Simultaneously, it eliminates the need for active RF transmission modules and laser weapons, solving the problem of interference signals easily spreading and affecting surrounding legitimate equipment, reducing electromagnetic radiation and self-exposure probability, decreasing countermeasure power consumption and per-use cost, making it suitable for low-cost UAV countermeasure scenarios, and effectively addressing the security threats posed by unauthorized and reckless low-altitude UAV flights. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a precision countermeasure system for unmanned aerial vehicles based on RIS metasurfaces according to the present invention. Figure 2 This is a flowchart of the signal sensing module of the present invention; Figure 3 This is a flowchart of the working process of a dual FPGA collaborative control module of the present invention; Figure 4 This is a schematic diagram of a RIS metasurface module according to the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been presented in the various embodiments of this application to enable the reader to better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments.

[0019] In the following description, certain specific details are set forth for the purpose of illustrating various disclosed embodiments in order to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the art will recognize that embodiments may be practiced without one or more of these specific details. In other instances, well-known apparatuses, structures, and techniques associated with this application may not have been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0020] Unless the context requires otherwise, throughout the specification and claims, the word “comprising” and its variations, such as “including” and “having”, shall be understood to have an open, inclusive meaning, that is, to be interpreted as “including, but not limited to”.

[0021] The embodiments of this application will be described in detail below with reference to the accompanying drawings to provide a clearer understanding of the purpose, features, and advantages of this application. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of this application, but are merely for illustrating the essential spirit of the technical solution of this application.

[0022] Throughout this specification, references to "an embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Therefore, the appearance of "in an embodiment" or "an embodiment" in various places throughout the specification does not necessarily refer to the same embodiment. Furthermore, a particular feature, structure, or characteristic may be combined in any manner in one or more embodiments.

[0023] To address the problems of inaccurate interference, reliance on active transmission, and high cost of existing countermeasure technologies, this invention proposes a precision UAV countermeasure system based on RIS metasurfaces, such as... Figure 1 As shown, it includes: The signal sensing module is responsible for transmitting the captured drone signals to the dual FPGA collaborative control module. The dual FPGA collaborative control module is responsible for sending the interference strategy generated based on the UAV signal to the RIS metasurface module. The RIS metasurface module generates and transmits directional countermeasure signals based on the aforementioned interference strategy. The feedback module monitors the countermeasure effect of the RIS metasurface module and feeds the monitoring data back to the dual FPGA collaborative control module to adjust the interference strategy and phase control commands. The power module provides power to the system.

[0024] The RIS metasurface module can employ electromagnetic control units distributed in an 8×8 array.

[0025] This invention achieves precise acquisition and strategy generation of UAV signals through the cooperation of a signal sensing module and a dual FPGA collaborative control module. It utilizes a RIS metasurface module to perform directional signal modulation, enabling precise directional transmission of countermeasure signals and strict control over the interference range. Simultaneously, it eliminates the need for active RF transmission modules and laser weapons, solving the problem of interference signals easily spreading and affecting surrounding legitimate equipment, reducing electromagnetic radiation and self-exposure probability, decreasing countermeasure power consumption and single-use cost, making it suitable for low-cost UAV countermeasure scenarios, and effectively addressing the security threats posed by unauthorized and reckless low-altitude UAV flights.

[0026] Furthermore, such as Figure 2 As shown, the signal sensing module includes a wideband radio frequency receiver and an analog-to-digital converter. The wideband radio frequency receiver captures UAV signals in real time, which are then converted into digital signals by the analog-to-digital converter and transmitted to the dual FPGA collaborative control module.

[0027] The signal sensing module also includes a miniature infrared sensor for enhancing drone signal capture in conjunction with the broadband radio frequency receiver at night or in low visibility conditions.

[0028] In one embodiment, the following implementation is given: The wideband RF receiver operates in the frequency band from 1.5GHz to 6GHz and includes a low-noise amplifier, a wideband signal receiving antenna, and an ADC sampling circuit. It is responsible for capturing enemy / target UAV communication signals, extracting the signal carrier-to-noise ratio, spectrum, and direction of arrival (DOA), and transmitting them to the dual FPGA collaborative control module. The miniature infrared sensor has the ability to capture signals at night, making up for the signal capture defects of a single RF sensor in nighttime or low-light environments.

[0029] During operation, the wideband RF receiver can be activated independently or simultaneously with the miniature infrared sensor to capture UAV communication signals in real time. It converts analog signals into 16-bit digital signals using a high-speed analog-to-digital converter chip, ensuring both accuracy and speed. The converted 16-bit digital signal is then transmitted to the dual FPGA collaborative control module via a high-speed serial bus, providing raw data for subsequent signal analysis and interference strategy generation.

[0030] By combining radio frequency and infrared dual detection, the problem of signal acquisition failure of a single sensor in complex environments is solved, and the stability and comprehensiveness of signal acquisition are improved.

[0031] Furthermore, such as Figure 3 As shown, the dual FPGA collaborative control module includes a master FPGA and a slave FPGA; The main control FPGA receives the UAV signal from the signal sensing module, extracts its features, calculates the distance and signal stability of the UAV based on the extracted features, selects an interference strategy, and transmits the interference strategy to the slave control FPGA. The slave FPGA performs metasurface phase calculation and encoding based on the interference strategy, and optimizes the phase using an improved genetic algorithm to prevent the countermeasure signal from spreading to non-target areas.

[0032] In one embodiment, the main control FPGA reads the UAV signal data transmitted by the signal sensing module in real time through a high-speed SPI interface, with a reading cycle of 100μs / frame to ensure data real-time performance. At the same time, the read data is preprocessed, including DC component removal and noise filtering, to remove interference noise from the signal and provide a clean signal source for subsequent feature extraction. The preprocessed data is stored in on-chip RAM, with 10 frames of data buffer reserved to avoid data loss.

[0033] Furthermore, the feature extraction extracted by the main control FPGA includes: It employs a parallel processing architecture to simultaneously extract signal frequency, modulation scheme, code rate, and signal strength.

[0034] Specifically: The preprocessed digital signal is processed by spectrum analysis, the center frequency of the signal is analyzed, and the result is stored in the on-chip RAM address. A deep learning-based convolutional neural network (CNN) classifier is used to pre-train a recognition model for common modulation types of drones. The model parameters are stored in on-chip Flash, and the recognition results are updated to the feature cache in real time. The signal transmission rate is obtained by a code rate detection algorithm based on autocorrelation function, with a code rate detection range of 10kbps~10Mbps, and the detection results are stored synchronously with the frequency data. The received signal strength (Pr) is extracted using an RSSI detection module with a detection range of -80dBm to -20dBm and a sampling frequency of 1MHz to ensure stable strength data.

[0035] Furthermore, the calculation of the UAV's distance and signal stability based on the extracted features includes: Based on the free space propagation loss model formula, the target distance is calculated in real time, and the calculation results are classified into short range, medium range and long range. Let the received UAV signal strength be Pr (unit: dBm), the equivalent radiated power of the transmitted signal be Pt (unit: dBm), the gains of the receiving antenna and the transmitting antenna be Gr and Gt (unit: dBi) respectively, and the extracted signal carrier frequency be f (unit: Hz).

[0036] Based on the above parameters, calculate the maximum allowable path loss: FSPL = Pt + Gt + Gr - Pr; And FSPL = 32.44 + 20log10(f) + 20log10(d); Get the target distance .

[0037] Generally, if d < 200m, it is considered a short distance. If 200 ≤ d < 500m, it is judged as a medium distance. If d ≥ 500m, it is considered a long distance; Those skilled in the art may also adjust the scope according to the actual situation.

[0038] Stability is determined according to preset criteria, and the results include high stability, medium stability, and low stability. The preset criteria include frequency stability, modulation consistency, signal strength fluctuation, and code rate stability. The judgment standards are as follows: Regarding frequency stability, if the center frequency deviation of multiple consecutive frames of signal is ≤ ±50kHz, it indicates that the UAV carrier frequency does not drift drastically and the frequency synchronization is good, thus meeting the criterion. For modulation consistency, i.e. the modulation type (QPSK, 16QAM, OFDM, FSK, etc.) remains unchanged for multiple consecutive frames, the criterion is met; For signal strength fluctuations, if the fluctuation of received signal strength (Pr) over multiple consecutive frames is ≤ ±6dB, it indicates that the UAV signal is stable and meets the criterion. For bitrate stability, the bitrate deviation of multiple consecutive frames is ≤±5%, which meets the criterion.

[0039] As described above, those skilled in the art can also adjust the numerical range according to the actual situation.

[0040] If all preset criteria are met, the system is considered highly stable; if one or two criteria are not met, the system is considered moderately stable; and if only one criterion is met, the system is considered lowly stable.

[0041] Furthermore, the interference strategy includes: Signal suppression jamming targets close-range, highly stable targets by directionally amplifying the drone's communication signals and reflecting them back to the drone. By superimposing the RIS full-unit in-phase coherent signal, the incident UAV communication signal is directionally reflected and greatly enhanced, forming a high-gain narrow beam that illuminates the target UAV receiver. This causes the front-end low-noise amplifier (LNA) to enter saturation overload, the signal-to-noise ratio to deteriorate sharply, and the UAV is unable to complete carrier acquisition, synchronization and demodulation. Ultimately, this leads to remote control disconnection, black screen in image transmission, and data transmission failure. In close-range scenarios, it can ensure accurate coverage of countermeasure signals, and the highly stable target status can ensure the continuous and stable output of signal suppression effect.

[0042] Signal command deception involves reflecting the drone's communication signal in reverse phase. The inverted signal is out of phase with the original carrier wave, and when they are superimposed, they cancel each other out, causing the receiver's carrier synchronization loop to lose lock. This is effective against medium-range and moderately stable targets. In mid-range scenarios, signal command deception strategies can adapt to transmission distance requirements while ensuring signal strength. A stable target state can maintain the continuity of signal cancellation effect and ensure the achievement of countermeasure objectives.

[0043] Spectrum obfuscation involves sending false remote control feedback signals or false positioning commands to drones, targeting distant and unstable targets. Additionally, frequency hopping, encryption, and highly jam-resistant drones utilize RIS metasurface fast random time-varying phase coding to broaden and diffuse the target's narrowband communication signal in the frequency domain, disrupting carrier synchronization, symbol synchronization, and frequency hopping pattern tracking. This prevents the receiver from acquiring a valid carrier, demodulating, and synchronizing, ultimately leading to a communication link interruption. In long-distance scenarios, spectrum obfuscation strategies can overcome distance limitations to achieve effective countermeasures. Under low-stability target conditions, the relevant control methods can flexibly adapt to signal changes to ensure the stability of the countermeasure effect.

[0044] Once the system is started, it can default to spectrum obfuscation and interference mode to continuously monitor long-distance, highly interference-resistant targets while waiting for signal feature extraction to be completed, providing data support for subsequent strategy switching; if a highly stable, close-range target is detected, an upswing switch is immediately triggered to prevent the target from escaping.

[0045] When a signal is detected to be continuously lost for more than a few frames, or the signal stability is continuously in a low stable state, the system will forcibly switch back to the spectrum scrambling interference mode and start the signal reacquisition program. The main control FPGA will increase the feature extraction frame length and perform moving average filtering. The slave control FPGA will maintain random phase transitions and continuously monitor the signal recovery status to ensure that effective interference can be started immediately after the signal is reacquisitioned.

[0046] When a sudden external interference (such as other electromagnetic signal interference) is detected, causing a sharp drop in signal stability, the main control FPGA immediately pauses the current interference strategy. After the signal stabilizes, it switches back to the corresponding strategy according to the current threat level to avoid the failure of countermeasures due to external interference.

[0047] The slave FPGA receives interference information transmitted from the master FPGA in real time via a high-speed LVDS interface, with the reception period matching the master FPGA's transmission period. Based on the received parameters, metasurface phase calculation and encoding are performed, as detailed below: To suppress signal interference, full-unit in-phase coding is adopted, and the phase of all 64 metasurface units is set to 0°, directly calling the preset in-phase coding template; To counter signal command deception, the phase code of each metasurface unit is calculated according to the phase compensation formula. To mitigate spectral confusion, random phase coding is employed. Based on the instantaneous phase formula for random phase transitions, the instantaneous phase of each metasurface unit is generated using a pseudo-random sequence generator (m-sequence) built into the FPGA.

[0048] Furthermore, the FPGA employs an improved genetic algorithm to calculate the optimal phase distribution of the RIS metasurface units. The core objective of the optimization is to ensure that the countermeasure signal can be directionally reflected to the target region while maintaining a sidelobe suppression ratio of no less than a certain value. This prevents the countermeasure signal from spreading to non-target regions and avoids electromagnetic interference. In one embodiment, this is achieved as follows: The improved genetic algorithm parameters are set as follows: population size 64, 50 iterations, crossover probability 0.8, mutation probability 0.05, and fitness function is "sidelobe suppression ratio ≥20dB + main lobe gain ≥25dB". Using the phase code calculated in the first step as the initial population, the phase distribution is iteratively optimized through selection, crossover, and mutation operations. After each iteration, the fitness value is calculated. If the fitness function requirements are met, the iteration is stopped immediately, and the optimal phase distribution is output. If the requirements are not met after 50 iterations, a preset backup phase distribution is used to ensure that it does not affect the interference operation. After optimization, the optimal phase distribution is simulated and verified, and the main lobe gain and side lobe suppression ratio are calculated. If the verification is successful, the optimal phase data is updated to the phase buffer and the initial phase code is replaced. If the verification fails, the optimization process is restarted.

[0049] Furthermore, such as Figure 4 As shown, the RIS metasurface module employs an array of electromagnetic control units, each of which includes a driving circuit and a PIN diode. The driving circuit is used to receive the interference strategy of the dual FPGA collaborative control module and control the PIN diode to complete the generation and transmission of directional countermeasure signals.

[0050] The RIS metasurface module adjusts the phase of each unit by switching the PIN diodes of each electromagnetic control unit according to the phase control command issued by the slave FPGA, so as to reflect the UAV communication signal in the direction of the target UAV and ensure that the signal can accurately cover the target.

[0051] Furthermore, after a certain period of time following the transmission of the countermeasure signal, the feedback module detects the signal strength of the drone. If the value drops to the threshold, the countermeasure is deemed successful, and a success signal is sent to the dual FPGA collaborative control module to reduce the strength of the interference strategy. If the value does not drop to the threshold, the countermeasure is deemed unsuccessful, and a failure signal is sent to the dual FPGA collaborative control module to regenerate the interference strategy until the countermeasure is successful.

[0052] The threshold can be set by those skilled in the art.

[0053] The feedback module uses a miniature radio frequency detector, which monitors the signal strength of the drone in the target area in real time.

[0054] The power module can be powered by 24V DC with a power output of less than 50W, employing a low-power design to reduce system operating costs. It also supports battery power, using a high-capacity lithium battery with a runtime of at least 8 hours, adapting to mobile deployment scenarios in the field and ensuring system deployment flexibility.

[0055] In a complete embodiment: The wideband RF receiver and miniature infrared sensor of the signal sensing module are activated simultaneously to capture the frequency-hopping signal of the encrypted frequency-hopping drone. This signal has a frequency range of 5.2 GHz to 5.8 GHz, a hopping period of 10 milliseconds, and a signal strength of -60 dBm. The signal sensing module performs feature extraction on the captured signal, extracting core features such as the signal frequency hopping period and signal strength. It then converts the analog signal into a 16-bit digital signal and transmits it to the main control FPGA via a high-speed serial bus.

[0056] After receiving the digital signal, the master FPGA analyzes the signal characteristics through spectrum analysis, modulation recognition, and code rate detection to determine that the UAV is a military encrypted frequency hopping UAV. Then, it calls the spectrum obfuscation strategy in the strategy library to generate a frequency hopping synchronization interference signal. The generation time is controlled within 0.1 seconds. Then, the interference signal is synchronously transmitted to the slave FPGA at a rate of 1Gbps.

[0057] After receiving the interference signal from the control FPGA, the electromagnetic response model of a 64-unit RIS metasurface is loaded. The model automatically matches parameters such as the target UAV's azimuth angle of 30 degrees and distance of 500 meters. An improved genetic algorithm is used to calculate the optimal phase distribution of the 64 electromagnetic control units, with the edge units set to 120 degrees and the center units to 60 degrees. After calculation, the phase data is converted into SPI control commands and sent to the 64-unit RIS metasurface module at a frequency of 10MHz.

[0058] After receiving SPI control commands, the 64-unit RIS metasurface module adjusts the phase of each electromagnetic control unit to directionally reflect interference signals containing spectral obfuscation information, accurately covering the target UAV. The feedback module's miniature RF detector monitors the UAV signal strength in real time. When the UAV signal strength drops to -108dBm, the signal blocking rate reaches 80%, indicating a successful countermeasure. The feedback module transmits the successful countermeasure signal to the dual FPGA collaborative control module, and the system stops the countermeasure operation, enters standby mode, and waits for the next UAV target to appear.

[0059] The solutions provided by the embodiments of this application have been described in detail above. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0060] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0061] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

Claims

1. A RIS metasurface-based UAV precision countermeasure system, characterized in that, include: The signal sensing module is responsible for transmitting the captured drone signals to the dual FPGA collaborative control module. The dual FPGA collaborative control module is responsible for sending the interference strategy generated based on the UAV signal to the RIS metasurface module. The RIS metasurface module generates and transmits directional countermeasure signals based on the aforementioned interference strategy. The feedback module monitors the countermeasure effect of the RIS metasurface module and feeds the monitoring data back to the dual FPGA collaborative control module to adjust the interference strategy and phase control commands. The power module provides power to the system.

2. The UAV precision countermeasure system based on RIS metasurface according to claim 1, characterized in that, The signal sensing module includes a wideband radio frequency receiver and an analog-to-digital converter. The wideband radio frequency receiver captures UAV signals in real time, which are then converted into digital signals by the analog-to-digital converter and transmitted to the dual FPGA collaborative control module.

3. The precision countermeasure system for unmanned aerial vehicles based on RIS metasurfaces according to claim 2, characterized in that, The signal sensing module also includes a miniature infrared sensor, which, in conjunction with the broadband radio frequency receiver, enhances the capture of drone signals at night or in low visibility conditions.

4. The RIS metasurface-based UAV precision countermeasure system of claim 1, wherein, The dual-FPGA collaborative control module includes a master FPGA and a slave FPGA; The main control FPGA receives the UAV signal from the signal sensing module, extracts its features, calculates the distance and signal stability of the UAV based on the extracted features, selects an interference strategy, and transmits the interference strategy to the slave control FPGA. The slave FPGA performs metasurface phase calculation and encoding based on the interference strategy, and optimizes the phase using an improved genetic algorithm to prevent the countermeasure signal from spreading to non-target areas.

5. The precision countermeasure system for unmanned aerial vehicles based on RIS metasurfaces according to claim 4, characterized in that, The feature extraction extracted by the main control FPGA includes: It employs a parallel processing architecture to simultaneously extract signal frequency, modulation scheme, code rate, and signal strength.

6. The precision countermeasure system for unmanned aerial vehicles based on RIS metasurfaces according to claim 5, characterized in that, The calculation of the UAV's distance and signal stability based on the extracted features includes: Based on the free space propagation loss model formula, the target distance is calculated in real time, and the calculation results are classified into short range, medium range and long range. Stability is determined according to preset criteria, and the results include high stability, medium stability, and low stability. The preset criteria include frequency stability, modulation consistency, signal strength fluctuation and code rate stability. If all preset criteria are met, the system is judged to be highly stable. If one or two criteria are not met, the system is judged to be moderately stable. If only one criterion is met, the system is judged to be lowly stable.

7. The RIS metasurface-based UAV precision countermeasure system of claim 6, wherein, The interference strategy includes: Signal suppression jamming targets close-range, highly stable targets by directionally amplifying the drone's communication signals and reflecting them back to the drone. Signal command deception, which reflects the drone's communication signals back to the drone in reverse, is used against medium-range and moderately stable targets; Spectrum obfuscation involves sending false remote control feedback signals or false positioning commands to the drone, targeting distant and unstable targets.

8. The precision countermeasure system for unmanned aerial vehicles based on RIS metasurfaces according to claim 1, characterized in that, The RIS metasurface module employs an array of distributed electromagnetic control units, each of which includes a driving circuit and a PIN diode. The driving circuit is used to receive the interference strategy of the dual FPGA collaborative control module and control the PIN diode to generate and transmit directional countermeasure signals.

9. The RIS metasurface-based UAV precision countermeasure system of claim 1, wherein, After a certain period of time following the transmission of the countermeasure signal, the feedback module detects the signal strength of the drone. If the value drops to the threshold, the countermeasure is deemed successful, and a success signal is sent to the dual FPGA collaborative control module to reduce the strength of the interference strategy. If the value does not drop to the threshold, the countermeasure is deemed unsuccessful, and a failure signal is sent to the dual FPGA collaborative control module to regenerate the interference strategy until the countermeasure is successful.

10. The RIS metasurface-based UAV precision countermeasure system of claim 1, wherein, The power module supports battery power.

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