Passive radio frequency guided single-face array radar collaborative anti-unmanned aerial vehicle system and method

Abstract

The invention relates to the field of wireless detection, and discloses a passive radio frequency guided single-sided array radar collaborative anti-unmanned aerial vehicle system and method, and the system comprises an electromagnetic field sensing module, a signal processing module, a radar system, a game theory decision module, a target recognition and confirmation module, and a self-adaptive control module. The method comprises the following steps: monitoring an electromagnetic environment in real time through an electromagnetic field sensing module and generating electromagnetic field data; according to the electromagnetic field data, the signal processing module adjusts the working frequency and waveform of the radar through a self-adaptive waveform optimization algorithm so as to avoid detection of electronic reconnaissance equipment of an enemy; through a single-sided array two-dimensional phased array radar, preliminary detection is carried out on a target in a passive radio frequency guide mode, and target positioning is carried out according to the optimized frequency and waveform. According to the invention, a passive radio frequency guiding mode is adopted, and through real-time electromagnetic field sensing and adaptive waveform optimization, the technical effect of significantly reducing the radar detection probability is achieved.

Description

Technical Field

[0001] The invention relates to the field of wireless detection, and in particular to a passive radio frequency guided single-sided array radar cooperative anti-UAV system and method. Background Art

[0002] With the rapid development of drone technology, the demand for anti-drone systems is increasing, especially in battlefields and sensitive areas, where enemy drones may attack key facilities or targets. Therefore, how to effectively intercept enemy drones has become a key issue in modern electronic warfare.

[0003] Most existing anti-UAV radar systems use active signal transmission to detect targets. Although this method can quickly detect targets, it also has significant defects. First, continuous active signal transmission can easily be captured by enemy electronic reconnaissance equipment, exposing the radar's position and increasing the risk of being hit or interfered. This is particularly dangerous in a battlefield environment, where the radar can be easily detected and destroyed by the enemy, seriously threatening the system's survivability.

[0004] In addition, although the traditional four-sided array radar structure can improve detection accuracy, its hardware is complex, costly, and bulky, making it difficult to flexibly deploy, especially in the civilian field. The high cost limits its widespread application. With the increase in drone threats, a low-cost, high-efficiency anti-drone system is urgently needed.

[0005] In addition, existing radar systems lack sufficient adaptability and usually rely on fixed frequencies and waveforms. In environments with strong enemy electronic interference, radar signals are easily interfered with and cannot effectively perform tasks. Many existing systems do not have adaptive adjustment capabilities, resulting in performance degradation in complex electromagnetic environments, or even loss of target tracking capabilities.

[0006] Finally, although some existing systems integrate target recognition and verification functions, most of them rely on a single sensor (such as radar or optoelectronic sensor), which limits the accuracy and reliability of target recognition in complex environments. In the case of strong interference, a single sensor may not be able to effectively identify the target, which is prone to misjudgment or missed judgment.

[0007] Therefore, the present invention proposes a passive RF guided single-array radar collaborative anti-UAV system and method to address the deficiencies of the prior art. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides a passive RF-guided single-array radar collaborative anti-UAV system and method, which solves the problems in the existing anti-UAV system that radar signals are easily detected by the enemy, the system is highly complex, the anti-interference capability is insufficient, and the target recognition accuracy is low.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a passive radio frequency guided single-sided array radar cooperative anti-UAV system, comprising: The electromagnetic field sensing module is used to monitor the electromagnetic field distribution in the environment in real time and generate electromagnetic field data, as well as to form electromagnetic interference conditions by capturing and analyzing the characteristics of electromagnetic interference signals; The signal processing module adjusts the radar operating frequency and waveform through an adaptive waveform optimization algorithm based on electromagnetic field data to avoid detection by enemy electronic reconnaissance equipment; The radar system uses a single-sided two-dimensional phased array radar, which adjusts the frequency and waveform through a signal processing module and detects targets in a passive RF guidance mode; Game theory decision module, used to adjust the radar working mode according to the enemy's electronic countermeasure strategy, and optimize the radar's operating frequency and waveform through real-time game analysis; The target identification and confirmation module is used to verify the location and type of the target through radar and electro-optical sensors after initial detection; The adaptive control module is used to adjust the radar signal transmission mode and frequency according to the real-time feedback of electromagnetic interference conditions to enhance the system's anti-interference ability.

[0010] Preferably, the electromagnetic field sensing module includes: Omnidirectional electromagnetic field sensing module, used to monitor the electromagnetic field distribution in the target airspace in real time; An electromagnetic signal acquisition module, used to receive and convert the electromagnetic signal collected by the omnidirectional electromagnetic field sensing module into electromagnetic field data; An electromagnetic data processing module is used to process electromagnetic field data and generate an electromagnetic field distribution map, from which the characteristics of enemy electronic reconnaissance signals are extracted; The data transmission module is used to transmit the processed electromagnetic field data to the signal processing module to analyze and optimize the radar operating frequency and waveform.

[0011] Preferably, the electromagnetic field sensing module captures electromagnetic signals in the target airspace in real time through the omnidirectional electromagnetic field sensor module, generates electromagnetic field data and performs analysis for calibrating the operating frequency and waveform of the radar signal.

[0012] Preferably, the signal processing module adopts an optimal control algorithm to generate an optimal operating frequency and waveform by analyzing real-time electromagnetic field data, which is used to encrypt and camouflage radar signals and block enemy electronic reconnaissance equipment from identifying radar signals.

[0013] Preferably, the steps of the optimal control algorithm are: Receive electromagnetic field data in real time and analyze the current electromagnetic environment; Identify the spectrum characteristics of enemy electronic reconnaissance equipment based on electromagnetic field data; Calculate the frequency range and waveform parameters of the radar signal; Adjusting the radar operating frequency and waveform according to the optimization result, wherein the optimization result is the optimal radar operating frequency and waveform parameters calculated and obtained through the optimal control algorithm according to the electromagnetic field data received in real time and the frequency spectrum characteristics of the enemy's electronic reconnaissance equipment; The adjusted operating frequency and waveform data are transmitted to the radar system for implementation.

[0014] Preferably, the game theory decision module calculates the enemy's electronic interference behavior in real time by establishing a zero-sum game model between the enemy and us, and adjusts the radar's operating frequency and waveform through a Nash equilibrium strategy.

[0015] Preferably, the target identification and confirmation module includes: A target preliminary detection module is used to perform preliminary detection of the target through the radar system and generate the position and preliminary characteristic information of the target; The photoelectric sensor cooperation module is used to detect the target through the photoelectric sensor and provide image data; The target matching and verification module is used to compare the target data provided by the radar and the optoelectronic sensor to verify the type and location of the target; The data fusion and confirmation module is used to fuse the verified target data with the radar detection data and perform the target validity judgment operation.

[0016] Preferably, the adaptive control module adjusts the radar's transmission power and waveform through an adaptive signal optimization algorithm according to the real-time feedback of interference intensity and target position information, and performs an operation to enhance the radar system's anti-interference capability.

[0017] Preferably, the interference intensity and target location information fed back in real time includes: Interference signal strength, used to indicate the interference signal strength and interference degree of the enemy's electronic interference source; Target location data, used to provide the target's real-time location coordinates and motion status; Jammer location, used to determine the location of enemy electronic jammers in space; Target motion trajectory, used to provide target movement path information; Interference spectrum information is used to analyze the spectrum characteristics of enemy interference signals.

[0018] The present invention provides a passive radio frequency guided single-sided array radar cooperative anti-UAV method, comprising the following steps: Monitor the electromagnetic environment in real time and generate electromagnetic field data through the electromagnetic field sensing module; Based on the electromagnetic field data, the signal processing module adjusts the radar's operating frequency and waveform through an adaptive waveform optimization algorithm to avoid detection by enemy electronic reconnaissance equipment; Through the single-sided two-dimensional phased array radar, the target is preliminarily detected in the passive RF guidance mode, and the target is located according to the optimized frequency and waveform; According to the enemy's electronic countermeasure strategy, the radar's operating mode and frequency are optimized in real time through the game theory decision module; Through the target recognition and confirmation module, radar and optoelectronic sensor data are combined to perform target matching and verification to verify the authenticity of the target; According to the feedback of interference intensity and target position information, the radar transmission mode and frequency are adjusted through the adaptive control module to enhance the system's anti-interference ability.

[0019] The present invention provides a passive radio frequency guided single-array radar cooperative anti-UAV system and method. It has the following beneficial effects: 1. The present invention achieves the technical effect of significantly reducing the probability of radar detection by adopting a passive RF guidance mode, through real-time electromagnetic field perception and adaptive waveform optimization. Compared with the shortcomings of the traditional radar system in the prior art that it needs to continuously and actively transmit signals and is easily detected and attacked by the enemy, the present invention reduces the active transmission time of the radar, making the radar system more concealed in front of the enemy's electronic reconnaissance equipment, and effectively avoiding the threat of enemy firepower strikes.

[0020] 2. The present invention adopts a single-array two-dimensional phased array radar structure and achieves ±60° azimuth coverage through space-time adaptive processing (STAP) algorithm, reducing the hardware complexity and overall cost of the system. Compared with the traditional four-array radar system, the design of the present invention greatly reduces the number of array units and does not require additional hardware expansion, saving equipment costs and simplifying the system maintenance and operation procedures.

[0021] 3. The present invention optimizes the radar's operating mode, frequency and waveform through the collaborative work of the game theory decision module and the adaptive control module, significantly enhancing the system's anti-interference ability. By dynamically adjusting the radar's transmission mode and frequency, the system can respond to the enemy's electronic countermeasure strategy in real time and avoid being suppressed by the enemy's jamming equipment. This technology solves the problem of the existing technology that radar is difficult to adapt to enemy interference in a complex electromagnetic environment, and improves the combat effectiveness of the anti-UAV system.

[0022] 4. The invention combines the collaborative work of radar and photoelectric sensors and adopts multimodal verification and data fusion technology to achieve high-precision identification and positioning of targets. Compared with the traditional target identification method that only relies on radar, the multi-sensor fusion of the present invention can effectively improve the accuracy of target identification, especially under low visibility or strong interference conditions, enhance the adaptability and reliability of the system, and ensure accurate tracking and verification of the target. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a system architecture diagram of the present invention; Figure 2 This is a schematic diagram of the structure of the electromagnetic field sensing module of the present invention; Figure 3 This is a schematic diagram of the structure of the target identification and confirmation module of the present invention; Figure 4 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1-Figure 3 The embodiment of the present invention provides a passive radio frequency guided single-sided array radar cooperative anti-UAV system, comprising: The electromagnetic field sensing module is used to monitor the electromagnetic field distribution in the environment in real time and generate electromagnetic field data, as well as to form electromagnetic interference conditions by capturing and analyzing the characteristics of electromagnetic interference signals; The electromagnetic field perception module is responsible for monitoring the electromagnetic field distribution in the environment and generating electromagnetic field data to provide data support for subsequent target identification, verification, frequency optimization, etc. Through effective electromagnetic field perception, enemy drones and other electronic jamming devices can be discovered and accurately identified in a timely manner, laying the foundation for the system's countermeasure function.

[0026] The electromagnetic field sensing module uses omnidirectional electromagnetic field sensing technology to sense the electromagnetic field distribution in the target airspace and generate electromagnetic field data. After processing, these data can provide accurate information about the electronic interference sources and potential targets in the environment. By combining protocol reverse analysis and radio frequency fingerprint recognition technology, the module can achieve efficient and reliable target detection in complex electromagnetic environments.

[0027] The core function of this module is to monitor the electromagnetic field distribution of the target airspace in real time and convert it into effective data, and further identify the electromagnetic characteristics of enemy electronic equipment through data processing and analysis. Next, these data will be transmitted to the signal processing unit to optimize the radar's operating frequency and waveform.

[0028] The electromagnetic field sensing module structure in this embodiment is as follows: Omnidirectional electromagnetic field sensing module, the omnidirectional electromagnetic field sensing module can fully capture electromagnetic signals from 360 degrees. Generally, this module performs spatial sampling through multiple sensor arrays to achieve omnidirectional perception of the target airspace. These sensors can capture low probability of intercept (LPI) signals, which are particularly suitable for detecting drone targets.

[0029] In some embodiments, the omnidirectional sensing module uses a broadband radio monitoring array that can scan the electromagnetic environment in real time over a wide frequency range and identify possible sources of enemy electronic reconnaissance signals.

[0030] Electromagnetic signal acquisition module, the electromagnetic signal acquisition module is responsible for receiving and converting the electromagnetic signals captured by the omnidirectional electromagnetic field sensing module into digital signals. This module uses a highly sensitive sensor to accurately capture weak electromagnetic waves and ensure the accuracy of data collection. In order to effectively capture weak electromagnetic waves, the acquisition module uses a high-gain antenna and a low-noise amplifier (LNA).

[0031] In some embodiments, the acquisition module includes sensors with multiple frequency ranges to ensure that common enemy electronic reconnaissance signal frequency bands can be covered, especially in complex electromagnetic interference environments, to ensure comprehensive data acquisition.

[0032] Electromagnetic data processing module: The electromagnetic data processing module is responsible for real-time processing of the collected electromagnetic field data, generating an electromagnetic field distribution map, and extracting the spectrum characteristics of enemy electronic equipment from it. The electromagnetic data processing module uses advanced digital signal processing (DSP) algorithms to filter noise and identify potential enemy signal sources.

[0033] In some embodiments, the data processing module uses a fast Fourier transform (FFT) algorithm to analyze the spectrum characteristics of the electromagnetic signal to identify the enemy's RF fingerprint. By comparing the RF fingerprint with the preset enemy signal library, the enemy's electronic interference source or drone target can be effectively identified. Through this process, the system can convert the electromagnetic field data into the characteristic information of the enemy signal, providing necessary support for target positioning and identification. The formula for Fourier transform is: ; in: represents the frequency domain signal, is the time domain signal, is the frequency, For time, is the kernel of the Fourier transform.

[0034] Data transmission module: The data generated by the electromagnetic field perception module is processed and then transmitted to the signal processing module by the data transmission module. This module adopts high-speed transmission technology to ensure low latency and high bandwidth of data transmission. By ensuring the reliability of data transmission, the electromagnetic field perception module can quickly respond to changes in the electromagnetic environment and provide data support for subsequent radar work and target identification.

[0035] The data transmission module can stably and efficiently transmit electromagnetic data to other parts of the system through optical fiber transmission or wireless communication technology in a complex electromagnetic environment, so as to reduce the increase in detection response time caused by data transmission delay.

[0036] In order to improve the system's adaptability in complex environments, the electromagnetic field perception module combines protocol reverse analysis technology and radio frequency fingerprint recognition technology. These technologies enable the system to not only capture enemy interference signals in an electromagnetic environment, but also analyze and identify their electromagnetic characteristics. By identifying enemy electronic signals, the electromagnetic field perception module can promptly report the enemy's electronic interference source, thereby providing accurate information for subsequent interference processing and target verification.

[0037] In addition, the system adopts a target detection strategy based on a feedback mechanism. When the electromagnetic field perception module detects a signal that meets the characteristics of a low probability of intercept, it triggers the radar system to perform further target detection. At this time, the radar will locate the target based on the optimized frequency and waveform. Through this feedback mechanism, the electromagnetic field perception module works in conjunction with the subsequent radar system to improve the accuracy of target identification and verification.

[0038] By introducing the electromagnetic field sensing module, the present invention achieves efficient target detection and accurate target positioning in complex electromagnetic environments. This module not only improves the detection accuracy of the system, but also effectively reduces the detection blind area caused by the complexity of the electromagnetic environment through its omnidirectional sensing capability. Combined with the optimal control algorithm and real-time feedback mechanism, the system can quickly adapt to the enemy's electronic interference strategy to ensure continuous and effective operation.

[0039] In practical applications, this electromagnetic field sensing module can not only deal with the threat of drones in complex battlefield environments, but can also be widely used in the monitoring and control of drones in the civilian field, which is of great significance.

[0040] The present invention combines omnidirectional electromagnetic field sensing, radio frequency fingerprint recognition and protocol reverse analysis through innovative electromagnetic field sensing technology, greatly improving the detection capability of the anti-UAV system in complex electromagnetic environments. By analyzing electromagnetic signals in real time, combined with subsequent radar detection and target recognition and verification modules, the system can achieve efficient and accurate target recognition and countermeasures, providing the system with strong anti-interference and survivability. This technical solution has broad application prospects in battlefield environments and civil UAV monitoring.

[0041] The signal processing module adjusts the radar operating frequency and waveform through an adaptive waveform optimization algorithm based on electromagnetic field data to avoid detection by enemy electronic reconnaissance equipment; The signal processing module is mainly responsible for processing the electromagnetic data provided by the electromagnetic field perception module and optimizing the radar's operating frequency and waveform based on this data. The role of the signal processing module is to ensure that the radar system can work effectively in an enemy electronic reconnaissance environment and minimize the risk of enemy detection. Through the optimization function of this module, the system can not only improve detection accuracy, but also improve the survivability of anti-UAVs, especially in a battlefield environment.

[0042] In one possible implementation, the signal processing module first adjusts the radar's operating frequency and waveform by combining the received electromagnetic field data with an adaptive waveform optimization algorithm. This module uses advanced algorithms to ensure that the radar's transmitted signal can adapt to the current electromagnetic environment. By optimizing the radar's operating parameters, the system can efficiently detect targets without being detected by enemy detection equipment.

[0043] The core algorithm of the signal processing module is the optimal control algorithm, which can calculate the optimal operating frequency and waveform in real time based on the electromagnetic field data. Specifically, when the electromagnetic field perception module captures the signal of the enemy's electronic reconnaissance equipment, the signal processing module will analyze these signals and calculate the frequency range and waveform that the radar signal should use. This optimization process can not only effectively avoid the enemy's spectrum monitoring, but also improve the radar's anti-interference ability in the enemy's interference environment.

[0044] In this embodiment, the signal processing module uses an adaptive waveform optimization algorithm, which determines the optimal frequency and waveform by analyzing electromagnetic field data in real time. The specific steps of the algorithm are as follows: Real-time reception of electromagnetic field data: The signal processing module first receives data from the electromagnetic field perception module. These data contain information about the electromagnetic environment in the target airspace, including the frequency, intensity and characteristics of the enemy's electronic interference signals.

[0045] Electromagnetic field feature extraction: After acquiring electromagnetic data, the module converts this data into frequency domain information to identify the spectrum characteristics of enemy electronic reconnaissance equipment. This process uses Fourier transform to perform spectrum analysis and calculate the frequency range and amplitude distribution of the target signal.

[0046] ; in: represents a frequency domain signal; is the time domain signal; is the frequency; For time; is the kernel of the Fourier transform.

[0047] Optimize frequency and waveform selection: By analyzing the spectrum characteristics, the signal processing module calculates a set of optimal operating frequencies and waveforms. The optimal control algorithm will adjust the frequency and waveform in time according to the dynamic changes of the electromagnetic environment to ensure that the radar signal can avoid the enemy's electronic reconnaissance.

[0048] Adjust radar frequency and waveform: After determining the optimal frequency and waveform, the signal processing module will pass these adjustment parameters to the radar system to ensure that the radar can work according to the optimized results.

[0049] In some embodiments, frequency optimization may be performed through dynamic frequency hopping or signal encryption technology. For example, the frequency of the radar signal may be hopped within a predetermined time to avoid being tracked or interfered by the enemy for a long time.

[0050] Specifically, the optimization of the signal processing module not only considers the radar's operating frequency and waveform, but also includes encryption and camouflage operations to make the radar signal more difficult for enemy electronic reconnaissance equipment to identify. In the enemy's electronic countermeasures, the radar's operating frequency and waveform must be able to change continuously to prevent being locked by enemy reconnaissance equipment. The signal processing module can dynamically adjust the radar's signal parameters according to environmental changes through optimal control algorithms and adaptive waveform optimization.

[0051] As an option, the signal processing module can also be combined with other anti-electronic warfare technologies, such as countermeasures for electronic jamming signals, to further enhance the anti-jamming capability of the radar system. For example, encryption and camouflage operations of radar signals can keep radar signals concealed within the receiving range of enemy electronic reconnaissance equipment, thereby improving the survivability of the system.

[0052] The collaborative work of the signal processing module and other modules is one of the core features of the present invention. The electromagnetic field perception module provides electromagnetic data. After optimization by the signal processing module, the radar system can detect and locate targets without being detected by enemy reconnaissance equipment. Under the influence of the enemy's electronic countermeasure strategy, the game theory decision module can adjust the radar working mode according to the real-time electromagnetic environment, and combine with the optimization results of the signal processing module to ensure that the radar system always maintains the optimal working state in a dynamic environment.

[0053] At the same time, the target recognition and confirmation module will accurately verify the initially detected target based on the frequency and waveform data provided by the signal processing module. Through multi-modal verification (including radar and optoelectronic system coordination), the authenticity and positioning accuracy of the target are ensured.

[0054] Through the optimization of the signal processing module, the entire anti-UAV system can achieve efficient target detection and precise positioning in a high-interference environment. In a battlefield environment, the system can continuously adjust the frequency and waveform of the radar signal to maintain a high degree of concealment and stability under the interference of enemy electronic reconnaissance equipment, ensuring rapid and accurate identification of targets.

[0055] In addition, the adaptive waveform optimization and encryption camouflage technology of the signal processing module enable the system to adjust the radar operating parameters in time when the enemy's electronic countermeasure strategy changes, thereby maintaining effective detection of the target. Through these optimization methods, the present invention significantly improves the anti-interference ability and survivability of the anti-UAV system in complex electromagnetic environments.

[0056] In this embodiment, the signal processing module can adjust the radar's operating frequency and waveform in real time according to the electromagnetic field data by using an adaptive waveform optimization algorithm and an optimal control algorithm, thereby improving the radar system's concealment and anti-interference capabilities. The module works in coordination with other modules, enabling the anti-UAV system to maintain efficient and accurate target detection and identification capabilities in complex electromagnetic environments, effectively responding to UAV threats in modern warfare and civilian fields.

[0057] The radar system uses a single-sided two-dimensional phased array radar, which adjusts the frequency and waveform through a signal processing module and detects targets in a passive RF guidance mode; The main function of the radar system is to use the optimized operating frequency and waveform to perform efficient target detection in the passive RF guidance mode. The radar system relies on a single-sided two-dimensional phased array radar structure, which uses advanced algorithms and hardware design to achieve accurate positioning of the target while minimizing the exposure of the radar system and the risk of enemy detection.

[0058] The radar system of this embodiment uses a single-sided two-dimensional phased array radar. The core advantage of this radar is that it can control the direction of the transmission beam by adjusting the phase difference of the transmitting unit, thereby achieving accurate detection of the target. Specifically, the radar system relies on the optimized frequency and waveform data provided by the signal processing module to perform airspace scanning and target positioning in the passive RF guidance mode.

[0059] In general, traditional radars use a multi-array radar structure, which usually requires multiple arrays to achieve a wider azimuth coverage. However, in the present invention, a single-array two-dimensional phased array radar structure is used. Compared with a four-array radar, a single-array radar has a lower hardware cost and a simpler design. The single-array radar adjusts the phase of each transmitting unit through phased array technology to achieve directional control of the beam, thereby accurately scanning the target area.

[0060] Specifically, the radar achieves beam direction control by adjusting the phase difference of the array unit. The phase difference calculation of each transmitting unit can be expressed as: ; in: is the phase difference of the transmitting unit; is the spacing between array elements, is the radar wavelength. Through this phase adjustment, the radar can scan in different directions, thereby achieving accurate detection of targets.

[0061] To improve the performance of the radar system, the present invention adopts the Space-Time Adaptive Processing (STAP) algorithm. The algorithm optimizes the radar's anti-interference capability and detection range by simultaneously processing the time and space domain information of the radar signal. The STAP algorithm is particularly suitable for complex electromagnetic environments and can significantly improve the detection accuracy of targets by optimizing the distribution of signals in the time and frequency domains.

[0062] With the help of the STAP algorithm, the radar system can detect in a larger azimuth range. Specifically, the STAP algorithm can achieve ±60° azimuth coverage without adding additional array units, effectively reducing hardware costs and system complexity.

[0063] The working principle of the STAP algorithm is based on optimizing the spatial and temporal characteristics of the target signal and the interference signal. The mathematical model is expressed as: ; in: is the receiving signal; is the spatial processing matrix; is the beamforming weight; is a noise signal. By minimizing interference noise, the STAP algorithm can improve signal quality and target positioning accuracy.

[0064] In this embodiment, the radar system works in the passive RF guidance mode, which means that the main signal of the system comes from the electromagnetic data of the electromagnetic field sensing module. At this time, the signal transmission frequency of the radar system is low, which can effectively reduce the detection probability of the enemy reconnaissance equipment.

[0065] Specifically, when the electromagnetic field sensing module captures the signal characteristics that meet the low probability of intercept (LPI) of the drone, it triggers the radar system to enter the target detection mode. Through the optimized frequency and waveform, the radar system can accurately scan the target area while avoiding long-term exposure to the monitoring range of the enemy radar.

[0066] During the target detection phase, the radar system uses beam dwell scanning technology to ensure efficient positioning of the target. Beam dwell scanning dynamically adjusts the phase difference of the array unit to focus the radar beam on the target area and enhance the detection sensitivity of the target.

[0067] This technology ensures that the radar system focuses on a specific target area, improves detection accuracy and reduces the overall energy consumption of the system. At the same time, the radar beam direction can be adjusted in real time based on the feedback electromagnetic data to optimize the detection effect.

[0068] In order to further improve the accuracy of target positioning and identification, the radar system of this embodiment works in conjunction with photoelectric sensors (such as visible light and infrared sensors). Specifically, the target position and preliminary information provided by the radar system are used by the photoelectric sensors for image verification and feature recognition. By comparing the photoelectric image data with the radar data, the system can accurately confirm the type, position and motion state of the target.

[0069] This multimodal target verification system significantly improves the reliability of target identification. Especially under complex electromagnetic interference and low visibility conditions, the optoelectronic system can effectively supplement the shortcomings of radar and further enhance the accuracy of target positioning.

[0070] Through the above-mentioned technical architecture and algorithm, the radar system of this embodiment significantly improves the target detection capability in a complex battlefield environment and effectively reduces the cost and complexity of the system.

[0071] Reduce system complexity: Compared with traditional four-sided array radars, the single-sided two-dimensional phased array radar structure reduces the hardware complexity of the system, and optimizes the azimuth coverage and anti-interference capabilities through the STAP algorithm.

[0072] Improve anti-interference capability: The combination of the STAP algorithm and the passive RF guidance mode enables the radar system to maintain high target detection accuracy in complex electromagnetic environments and greatly reduces the probability of being discovered by enemy electronic reconnaissance equipment.

[0073] Enhanced target identification accuracy: Combining the data from the optoelectronic system and radar system can effectively verify the type and location of the target, improving the accuracy of target identification.

[0074] The radar system of this embodiment works closely with system components such as the electromagnetic field sensing module, the signal processing module, and the game theory decision module. With the support of the electromagnetic data provided by the electromagnetic field sensing module, the signal processing module optimizes the radar signal and transmits it to the radar system for target detection. The game theory decision module dynamically adjusts the radar's operating mode and frequency according to the enemy's electronic interference behavior to ensure that the system maintains optimal performance in complex environments.

[0075] Through the collaborative work between various modules, the system can respond to changes in the electromagnetic environment in real time, ensuring that anti-UAV missions can be effectively performed in any environment.

[0076] This embodiment significantly improves the performance of the anti-UAV system by adopting technologies such as single-sided two-dimensional phased array radar, space-time adaptive processing algorithm (STAP), and passive RF guidance mode. The radar system can efficiently detect and accurately locate targets in complex electromagnetic environments, while reducing the cost and complexity of the system. By coordinating with the optoelectronic system, the accuracy of target recognition is further enhanced. The system has strong adaptability and high concealment, and has broad application prospects.

[0077] Game theory decision module, used to adjust the radar working mode according to the enemy's electronic countermeasure strategy, and optimize the radar's operating frequency and waveform through real-time game analysis; The game theory decision module is mainly responsible for adjusting the radar working mode in real time according to the enemy's electronic countermeasure strategy and optimizing the radar's operating frequency and waveform. The purpose of the game theory decision module is to dynamically respond to the enemy's electronic interference behavior by establishing a zero-sum game model between the enemy and us, and ensure that the radar system can operate efficiently in a complex electromagnetic environment. This module works closely with the signal processing module and the radar system to jointly optimize the overall performance of the system.

[0078] Generally speaking, radar systems face multiple electronic interferences in complex battlefield environments, especially in electronic countermeasure environments, where the enemy may use different interference strategies to suppress the radar. The main function of the game theory decision module is to adjust the radar's operating mode and frequency in real time according to the enemy's electronic interference behavior to ensure that the radar can continue to work effectively under the enemy's electronic countermeasures.

[0079] In some embodiments, the game theory decision module makes adversarial decisions by establishing a zero-sum game model between the enemy and the friendly party. The model is dynamically optimized based on the strategy choices and corresponding benefits of the enemy and the friendly party. The essence of the zero-sum game model is that in the process of confrontation between the two parties, the gain of one party comes from the loss of the other party. Therefore, the goal of the system is to maximize its own benefits, that is, to improve the detection performance of the radar while reducing the risk of being detected by the enemy.

[0080] Implementation of game theory decision module: Establish a zero-sum game model. In the process of confrontation between the enemy and us, the game theory decision module first establishes a zero-sum game model based on the enemy's interference strategy. The basic form of the zero-sum game model is: ; in: represents our utility function, which defines our benefits under a given strategy combination; represents the enemy's utility function, which defines the enemy's payoff under a given strategy combination; Indicates the strategy we choose (such as the radar's operating mode and frequency); Indicates the jamming strategy chosen by the enemy.

[0081] Analyze the enemy's electronic jamming strategy. In the game theory decision module, first analyze the enemy's jamming strategy. The enemy's electronic jamming strategy usually relies on its ability to perceive our radar system. The enemy may affect the operation of our radar through constantly changing jamming signals, spectrum occupancy, and electronic attack methods (such as jamming, deception, etc.).

[0082] In some cases, the enemy may use frequency hopping, pulse interference and other means to counter, and our radar system needs to make corresponding adjustments to minimize the impact of interference.

[0083] Game analysis and strategy optimization, the game theory decision module optimizes the strategy based on the enemy's interference strategy and our optional response strategy through real-time game analysis. Specifically, the module will calculate the optimal radar working mode and frequency in real time, so that our radar can maintain the most efficient working state under enemy interference.

[0084] As an option, the game theory decision module can optimize the radar's operating frequency and waveform through the Nash equilibrium algorithm. In game theory, Nash equilibrium refers to a strategy combination in which no party can obtain a better result by unilaterally changing its strategy. In this system, Nash equilibrium means that when we choose the radar operating mode, we can avoid enemy interference to the greatest extent and optimize detection performance.

[0085] The mathematical expression of Nash equilibrium is: ; in: is our optimal strategy, which means the best radar working mode and frequency selection we adopt when the enemy's interference strategy is known; is the enemy's optimal jamming strategy, which means the best jamming strategy adopted by the enemy when the working mode and frequency of our radar are known; is our utility function under the optimal strategy combination; is our utility function under the non-optimal strategy.

[0086] Optimize the radar's operating mode and frequency. Based on the results of game theory decision analysis, the radar system's operating mode and frequency will be dynamically optimized. Specifically, the radar's operating frequency and waveform will be continuously adjusted according to the enemy's jamming strategy. For example, if the enemy uses frequency hopping technology, our radar can choose an operating frequency band that does not overlap with the enemy's jamming signal frequency; if the enemy uses pulse jamming, our radar may adjust to continuous wave mode or change the pulse width to avoid interference.

[0087] The game theory decision module in this embodiment provides several significant advantages: Real-time dynamic adjustment: By establishing a zero-sum game model, the system can adjust the radar's operating mode and frequency in real time when the enemy's electronic countermeasure behavior changes. This dynamic adjustment ensures the radar's adaptability in a highly dynamic environment.

[0088] Improve anti-interference capability: Through the real-time optimization of enemy and friendly strategies by the game theory decision-making module, the radar system can maintain the optimal working state in a complex electronic interference environment, thus enhancing the system's anti-interference capability.

[0089] Avoid detection: In the process of game analysis, the radar system can minimize the risk of being exposed to enemy reconnaissance equipment. This "stealthiness" is achieved by optimizing the radar operating frequency, waveform and other parameters, thereby avoiding passive exposure of radar signals.

[0090] The game theory decision module works closely with other modules (such as the electromagnetic field perception module, signal processing module, and radar system) to jointly achieve the efficient combat capability of the anti-UAV system. The electromagnetic field perception module provides electromagnetic environment information for the radar, the signal processing module optimizes the frequency and waveform of the radar signal, and the game theory decision module optimizes the working mode of the radar system according to the enemy's electronic interference behavior. Through the collaborative work between these modules, the system can maintain efficient and concealed target detection and positioning capabilities in the battlefield environment.

[0091] Through real-time game analysis and optimization of the game theory decision module, the anti-UAV system of the present invention can dynamically adjust the working mode and frequency of the radar according to the enemy's electronic jamming strategy. This mechanism greatly improves the anti-jamming ability and survivability of the radar system, ensuring that the radar can continue to work stably under strong enemy electronic jamming. The design of this module not only enhances the combat effectiveness of the anti-UAV system, but also enables the system to better adapt to the complex and changing battlefield environment.

[0092] The target identification and confirmation module is used to verify the location and type of the target through radar and electro-optical sensors after initial detection; The target recognition and confirmation module is mainly responsible for verifying the type, location and validity of the target through the coordinated work of the radar system and the photoelectric sensor, ensuring that the system can accurately identify and confirm the target. The target recognition and confirmation module works closely with the electromagnetic field perception module, signal processing module, and game theory decision module to jointly improve the system's detection capability and survivability.

[0093] Generally speaking, in battlefield environments, due to the complexity and diversity of electronic countermeasures, target identification and verification often face great challenges. To solve this problem, the target identification and confirmation module uses radar and optoelectronic sensors to work together to verify targets based on electromagnetic characteristics, radar reflection characteristics and optical characteristics, thereby ensuring the accuracy and effectiveness of the target.

[0094] In this embodiment, the target recognition and confirmation module works together through the following submodules: The target preliminary detection module uses a single-sided two-dimensional phased array radar to perform preliminary detection of the target. The preliminary detection is carried out through the electromagnetic wave emission and reflection of the radar system. The module preliminarily determines the location and basic feature information of the target based on the reflection characteristics of the radar echo signal.

[0095] In some embodiments, the reflection intensity of the radar signal is related to the electromagnetic characteristics of the target. The radar detects information such as the distance and speed of the target through the reflected signal. For example, although the radar waveform reflections of drones and flying birds are similar in some aspects, the radar system can accurately estimate the distance and speed of the target through multiple measurements and signal processing, combined with other characteristics (such as the target's motion trajectory and position changes).

[0096] Photoelectric sensor collaborative module, the photoelectric sensor collaborative module further detects the target through infrared or visible light cameras. The photoelectric sensor module can provide high-resolution image data to further verify the location and characteristics of the target.

[0097] In some embodiments, the photoelectric sensor can provide accurate imaging within the target area detected by the radar, helping the system to further distinguish the target from the surrounding environment. For example, the system can confirm the specific type of target, such as a drone, a bird, or other types of targets, by combining image data with radar data.

[0098] The target matching and verification module compares the target data obtained by the radar and the photoelectric sensor to verify the type and location of the target. The core task of this module is to ensure the accuracy of the target recognition result by comparing multi-source data.

[0099] In this embodiment, the system uses a target feature matching algorithm (such as a template matching algorithm or a target recognition algorithm based on deep learning) to match the radar reflection data with the photoelectric sensor image to determine whether they belong to the same target.

[0100] For example, in some embodiments, if the radar system detects a target and obtains image data of the target through the photoelectric sensor, the target matching and verification module will compare the two to ensure the consistency of the target position and type.

[0101] Data fusion and confirmation module, the data fusion and confirmation module fuses the verified target data with the radar detection data to determine the target's effectiveness. By fusing multiple data sources (radar data, optoelectronic image data, etc.), the module can more accurately determine the effectiveness of the target.

[0102] In some embodiments, data fusion uses algorithms such as weighted average method or Kalman filter to comprehensively consider the accuracy and reliability of radar data and optoelectronic sensor data. These fusion algorithms can effectively remove noise and improve the accuracy of target recognition.

[0103] For example, if the target position detected by the radar is highly consistent with the target position in the image data provided by the photoelectric sensor, the system will confirm the validity of the target and further initiate subsequent processing steps.

[0104] Through the collaborative work of radar and photoelectric sensors, the target recognition and confirmation module can effectively solve the following problems: Improve the accuracy of target recognition: Through the fusion and comparison of multi-source data, the system can more accurately identify targets in complex environments and avoid misjudgment or omission.

[0105] Enhance the system's anti-interference ability: Since the system not only relies on radar signals, but also uses photoelectric sensors for auxiliary verification, even when the radar signal is interfered with, the photoelectric sensors can still provide effective information support to ensure the reliability of the system.

[0106] Provide accurate target positioning: Through the combination of radar and optoelectronic data, the system can accurately locate the target position, improving the combat effectiveness of the entire system.

[0107] In some embodiments, the target recognition and confirmation module can be further expanded to integrate more sensors, such as LiDAR, to enhance the multi-dimensional detection capability of the system. Through the integration of multiple sensors, the system can fully verify the target from multiple angles, improving the accuracy of target recognition and the adaptability of the system.

[0108] In addition, with the continuous development of drone technology, the target recognition and confirmation module can also be continuously optimized and upgraded. For example, in future implementations, target recognition algorithms based on artificial intelligence (AI) or deep learning may be introduced to further improve the system's recognition capabilities in complex environments.

[0109] The target recognition and confirmation module ensures that the anti-UAV system can accurately identify the target through the fusion and comparison of multi-source data. Through the collaborative work of radar and optoelectronic sensors, the system not only improves the accuracy and reliability of target recognition, but also enhances the system's adaptability in complex environments. This modular design enables the system to operate efficiently in battlefield environments and civilian fields, meeting the needs of practical applications.

[0110] Adaptive control module, used to adjust the radar signal transmission mode and frequency according to the real-time feedback of electromagnetic interference, and enhance the anti-interference ability of the system; The adaptive control module is used to dynamically adjust the radar system's transmission mode and frequency according to the electromagnetic interference situation and the target's real-time location information, significantly enhancing the anti-interference capability of the anti-UAV system and improving the ability to accurately track the target. In a complex electromagnetic environment, the module can intelligently optimize the radar's operating parameters based on the feedback of interference intensity data and target location information to ensure the system's effective operation in a battlefield environment.

[0111] The adaptive control module optimizes system performance in the following ways: Real-time feedback interference intensity processing. In this embodiment, the system obtains the electromagnetic interference signal intensity of the target area in real time through the electromagnetic field sensing module. Based on the feedback data, the control module will adjust the radar's operating frequency and transmission mode according to the intensity and type of the interference signal. This adjustment can effectively avoid the enemy's electronic reconnaissance equipment from detecting the radar signal and ensure the radar's concealment.

[0112] Among them, the strength of the interference signal It can be characterized by the amplitude and spectrum characteristics of the electromagnetic signal. The specific interference intensity model can be expressed as: ; in: Represents the total energy or power of the interference signal, usually expressed as the integral result of the signal; Indicates time The received signal on the receiver is usually composed of interference sources or received electromagnetic wave signals; is the square of the signal’s amplitude, which represents the signal’s energy; It is a time variable, which indicates the change of the signal in the time domain.

[0113] Target position information processing and radar operation optimization. In this embodiment, the adaptive control module can also adjust the radar's operating mode and transmission waveform according to the target's position information. By cooperating with the target recognition module, the system obtains the target's spatial position in real time. and the target's motion state.

[0114] For example, the target's location information It can be represented as three-dimensional coordinates that change over time. The dynamic information of the target enables the system to intelligently determine the trajectory of the target and make timely adjustments to the radar's operating mode. For example, when the target's position information shows that the target is approaching the radar quickly, the system will automatically adjust the radar's scanning speed and beam width to ensure that the target is always within the radar's scanning range.

[0115] Formulaically, the spatial position of the target It can be expressed by the following relationship: ; in: Indicates at time The three-dimensional coordinate point or position vector under this condition usually represents a position in space or the distribution of a signal in three-dimensional space, which depends on time; The time The values ​​of the three coordinate axes at the time (usually in three-dimensional space) Axis), the values ​​of these coordinate axes can represent the position of the target in space, or represent certain characteristics of the signal.

[0116] Dynamic adjustment of radar transmission mode and frequency: According to the target's location information and electromagnetic interference, the adaptive control module dynamically adjusts the radar's transmission frequency and waveform. Especially in high-interference environments, the system can avoid spectrum overlap with enemy electronic reconnaissance equipment by adjusting the frequency and waveform.

[0117] In this case, the adaptive control module uses an adaptive signal optimization algorithm to intelligently select the frequency and waveform that best suits the current interference conditions to improve the performance of the radar in complex electromagnetic environments.

[0118] For example, the system adjusts the radar frequency and waveform parameters These adjustments are usually made based on real-time feedback data and optimization algorithms. For example: ; in: is the initial frequency of the radar; The frequency increment is adjusted based on the real-time electromagnetic environment. It is dynamic and continuously adjusted according to the actual interference intensity and target location.

[0119] Radar signal recovery and precise target tracking: Under high-intensity interference conditions, the radar may lose its ability to effectively track the target. The adaptive control module can restore the radar signal and ensure accurate target tracking through real-time feedback of target position information and interference data.

[0120] For example, when the radar loses target tracking, the system will adjust the radar waveform and transmission power according to the target's motion trajectory and interference environment. , ensuring the signal is restored in the optimal transmission mode. Adjusted target position It can be expressed as: ; in: is the target original position; is the adjusted target position; It is the correction factor adjusted according to the real-time interference situation.

[0121] Through the above-mentioned adaptive control strategy, the adaptive control module of this embodiment has the following technical advantages: Enhanced anti-interference capability: Through real-time monitoring and feedback of electromagnetic interference signal strength, the system can dynamically adjust the radar's operating frequency and transmission mode to avoid spectrum overlap with enemy reconnaissance equipment, thereby effectively avoiding detection.

[0122] Accurate target tracking capability: Combining the target's real-time position information and dynamically adjusting radar parameters, the system can ensure accurate tracking of the target in complex electromagnetic environments, especially maintaining stable tracking of the target when there is strong enemy interference.

[0123] Reduction of system complexity and cost: Through the use of intelligent algorithms, the radar system can improve its adaptability and performance without increasing hardware costs, reducing the complexity and overall cost of the system.

[0124] Future implementations can further utilize machine learning algorithms to optimize adaptive control strategies, especially in complex interference environments, to automatically select the most suitable radar operating parameters. In addition, through long-term learning of target trajectories, the system can also predict the future position of the target, so that adjustments can be made in advance, further improving the system's response speed and accuracy.

[0125] The adaptive control module is one of the core modules of the present invention. It significantly enhances the anti-interference and target tracking capabilities of the system by adjusting the radar's operating frequency, transmission mode and signal recovery in real time. Whether in civilian or battlefield environments, this system can efficiently cope with complex electromagnetic interference and improve the combat effectiveness of the anti-UAV system.

[0126] See also Figure 4 The present invention also provides a passive radio frequency guided single-sided array radar cooperative anti-UAV method, comprising the following steps: S1, monitor the electromagnetic environment in real time and generate electromagnetic field data through the electromagnetic field sensing module; S2. Based on the electromagnetic field data, the signal processing module adjusts the radar's operating frequency and waveform through an adaptive waveform optimization algorithm to avoid detection by enemy electronic reconnaissance equipment; S3, using a single-sided two-dimensional phased array radar, conduct preliminary detection of the target in passive RF guidance mode, and locate the target based on the optimized frequency and waveform; S4, according to the enemy's electronic countermeasure strategy, optimize the radar's operating mode and frequency in real time through the game theory decision module; S5, through the target recognition and confirmation module, combine the radar and optoelectronic sensor data to perform target matching and verification to verify the authenticity of the target; S6. According to the feedback interference intensity and target position information, the radar transmission mode and frequency are adjusted through the adaptive control module to enhance the system's anti-interference ability.

[0127] The main purpose of step S1 is to monitor the electromagnetic environment of the target airspace and its surroundings in real time through the electromagnetic field perception module. The electromagnetic field perception module usually includes a group of omnidirectional electromagnetic field sensors, which can capture and analyze electromagnetic radiation from the target, enemy electronic interference sources or other signal sources. These sensors collect electromagnetic signals through radio frequency (RF) and convert them into electromagnetic field data. This data is obtained through multi-channel electromagnetic signal reception, filtering and enhancement. After the data is generated, it will become an important basis for the subsequent optimization of the radar operating frequency and waveform. The realization of this process helps to provide accurate electromagnetic environment analysis for subsequent steps, helping the system to select the best operating parameters to avoid enemy detection.

[0128] For the electromagnetic field data obtained in step S1 in step S2, the signal processing module uses an adaptive waveform optimization algorithm to adjust the radar's operating frequency and waveform. The goal of this adjustment is to ensure that the radar signal can avoid the enemy's electronic reconnaissance equipment and avoid detection. The adaptive waveform optimization algorithm will analyze the response characteristics of the enemy's reconnaissance equipment and the changes in the electromagnetic environment based on the received electromagnetic field data, and adjust the transmission frequency and waveform of the radar signal accordingly to ensure that the radar operates at the lowest probability of detectability (LPI). The algorithm reduces the risk of radar exposure by optimizing the radar waveform and frequency, thereby improving the concealment of the system.

[0129] After completing the electromagnetic field data analysis and signal optimization in step S3, this step uses the single-sided two-dimensional phased array radar to enter the passive RF guidance mode and start preliminary detection of the target. In this mode, the radar scans suspicious targets in the airspace by adjusting the beam direction. When the target signal meets the preset low probability of intercept (LPI) standard, the radar will continue to track the target and accurately locate it based on the optimized frequency and waveform. In this way, the radar can complete the preliminary detection and precise positioning of the target without exposing its position, ensuring the accurate acquisition of target information.

[0130] For step S4, this step analyzes the enemy's electronic interference behavior and electronic countermeasure strategy in real time through a game theory decision module. This module establishes a zero-sum game model between the enemy and us, and predicts its actions by calculating in real time the electronic interference strategies that the enemy may take. The game theory model will take into account the response strategy of the radar and adjust its operating mode and frequency to counter the enemy's electronic warfare behavior. In some embodiments, the game theory decision module adopts a Nash equilibrium strategy, that is, while ensuring the concealment of radar operation, it maximizes the interference suppression effect. Through game analysis, the radar system can dynamically adapt to the electronic warfare environment, thereby improving the survivability of the system.

[0131] For step S5, in this step, the target recognition and confirmation module combines the data of the radar and the photoelectric sensor to verify the initially detected target. First, the radar obtains the rough position information of the target through the reflected wave, and then the photoelectric sensor (such as an infrared camera and a visible light camera) obtains a high-precision image of the target. The image data obtained by the photoelectric sensor can effectively match the features of the target to further verify whether it is a real target. The target verification module fuses the two types of sensor data and uses the target matching algorithm to confirm the location and type of the target. If the radar and photoelectric sensor data match well and the target meets the preset classification criteria, the system can determine that the target is a real target.

[0132] At the end of step S6, based on the feedback information obtained in the previous step (including interference intensity and target location information), the adaptive control module will adjust the radar's transmission mode and frequency according to the real-time changing interference situation. This module dynamically adjusts the radar's working state to cope with complex electronic interference environments by analyzing the location and intensity of the interference source and the movement trajectory of the target. The adaptive control module can respond to changes in interference intensity in real time, enhance the radar system's anti-interference ability, and improve signal reliability. This adjustment can effectively prevent enemy electronic interference equipment from damaging the radar system and ensure the stability and accuracy of target tracking.

[0133] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Passive RF guided single array radar cooperative anti-UAV system, characterized by: include: The electromagnetic field sensing module is used to monitor the electromagnetic field distribution in the environment in real time and generate electromagnetic field data, as well as to form electromagnetic interference conditions by capturing and analyzing the characteristics of electromagnetic interference signals; The signal processing module adjusts the radar operating frequency and waveform through an adaptive waveform optimization algorithm based on electromagnetic field data to avoid detection by enemy electronic reconnaissance equipment; The radar system uses a single-sided two-dimensional phased array radar, which adjusts the frequency and waveform through a signal processing module and detects targets in a passive RF guidance mode; Game theory decision module, used to adjust the radar working mode according to the enemy's electronic countermeasure strategy, and optimize the radar's operating frequency and waveform through real-time game analysis; The target identification and confirmation module is used to verify the location and type of the target through radar and electro-optical sensors after initial detection; The adaptive control module is used to adjust the radar signal transmission mode and frequency according to the real-time feedback of electromagnetic interference conditions to enhance the system's anti-interference ability.

2. The passive radio frequency guided single-surface radar cooperative anti-UAV system according to claim 1 is characterized in that: The electromagnetic field sensing module comprises: Omnidirectional electromagnetic field sensing module, used to monitor the electromagnetic field distribution in the target airspace in real time; An electromagnetic signal acquisition module, used to receive and convert the electromagnetic signal collected by the omnidirectional electromagnetic field sensing module into electromagnetic field data; An electromagnetic data processing module is used to process electromagnetic field data and generate an electromagnetic field distribution map, from which the characteristics of enemy electronic reconnaissance signals are extracted; The data transmission module is used to transmit the processed electromagnetic field data to the signal processing module to analyze and optimize the radar operating frequency and waveform.

3. The passive radio frequency guided single-surface array radar cooperative anti-UAV system according to claim 1 is characterized in that: The electromagnetic field sensing module captures the electromagnetic signal of the target airspace in real time through the omnidirectional electromagnetic field sensor module, generates and analyzes the electromagnetic field data, and is used to calibrate the operating frequency and waveform of the radar signal.

4. The passive radio frequency guided single-surface array radar cooperative anti-UAV system according to claim 1 is characterized in that: The signal processing module adopts an optimal control algorithm to generate an optimal operating frequency and waveform by analyzing real-time electromagnetic field data, which is used to encrypt and camouflage radar signals and block enemy electronic reconnaissance equipment from identifying radar signals.

5. The passive radio frequency guided single-array radar cooperative anti-UAV system according to claim 4 is characterized in that: The steps of the optimal control algorithm are: Receive electromagnetic field data in real time and analyze the current electromagnetic environment; Identify the spectrum characteristics of enemy electronic reconnaissance equipment based on electromagnetic field data; Calculate the frequency range and waveform parameters of the radar signal; Adjusting the radar operating frequency and waveform according to the optimization result, wherein the optimization result is the optimal radar operating frequency and waveform parameters calculated and obtained through the optimal control algorithm according to the electromagnetic field data received in real time and the frequency spectrum characteristics of the enemy's electronic reconnaissance equipment; The adjusted operating frequency and waveform data are transmitted to the radar system for implementation.

6. The passive radio frequency guided single-surface radar cooperative anti-UAV system according to claim 1 is characterized in that: The game theory decision module calculates the enemy's electronic interference behavior in real time by establishing a zero-sum game model between the enemy and us, and adjusts the radar's operating frequency and waveform through the Nash equilibrium strategy.

7. The passive radio frequency guided single-surface array radar cooperative anti-UAV system according to claim 1 is characterized in that: The target identification and confirmation module includes: A target preliminary detection module is used to perform preliminary detection of the target through the radar system and generate the position and preliminary characteristic information of the target; The photoelectric sensor cooperation module is used to detect the target through the photoelectric sensor and provide image data; The target matching and verification module is used to compare the target data provided by the radar and the optoelectronic sensor to verify the type and location of the target; The data fusion and confirmation module is used to fuse the verified target data with the radar detection data and perform the target validity judgment operation.

8. The passive radio frequency guided single-surface array radar cooperative anti-UAV system according to claim 1 is characterized in that: The adaptive control module adjusts the radar's transmission power and waveform according to the real-time feedback of interference intensity and target position information through an adaptive signal optimization algorithm, and performs an operation to enhance the radar system's anti-interference capability.

9. The passive radio frequency guided single-surface radar cooperative anti-UAV system according to claim 8 is characterized in that: The real-time feedback of interference intensity and target location information includes: Interference signal strength, used to indicate the interference signal strength and interference degree of the enemy's electronic interference source; Target location data, used to provide the target's real-time location coordinates and motion status; Jammer location, used to determine the location of enemy electronic jammers in space; Target motion trajectory, used to provide target movement path information; Interference spectrum information is used to analyze the spectrum characteristics of enemy interference signals.

10. A passive RF-guided single-sided array radar cooperative anti-UAV method, applied to the passive RF-guided single-sided array radar cooperative anti-UAV system as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Monitor the electromagnetic environment in real time and generate electromagnetic field data through the electromagnetic field sensing module; Based on the electromagnetic field data, the signal processing module adjusts the radar's operating frequency and waveform through an adaptive waveform optimization algorithm to avoid detection by enemy electronic reconnaissance equipment; Through the single-sided two-dimensional phased array radar, the target is preliminarily detected in the passive RF guidance mode, and the target is located according to the optimized frequency and waveform; According to the enemy's electronic countermeasure strategy, the radar's operating mode and frequency are optimized in real time through the game theory decision module; Through the target recognition and confirmation module, radar and optoelectronic sensor data are combined to perform target matching and verification to verify the authenticity of the target; According to the feedback of interference intensity and target position information, the radar transmission mode and frequency are adjusted through the adaptive control module to enhance the system's anti-interference ability.

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