Method and system for anti-electromagnetic interference of unmanned aerial vehicle

Abstract

The application provides a method and system for resisting electromagnetic interference of a UAV, and relates to the technical field of anti-interference. An electromagnetic signal data of an environment is collected by a sensor array arranged on a UAV platform; the collected electromagnetic signal data is processed to identify an interference type and estimate a direction of an interference source; a result of the interference feature analysis step and current flight state information of the UAV are fused to evaluate and output an electromagnetic threat level; according to the electromagnetic threat level, a corresponding anti-interference strategy is called and executed from a strategy library, an intelligent anti-interference decision and control subsystem cooperates with a flight module to respond; and the electromagnetic environment change and the execution effect of the anti-interference strategy are continuously monitored, and the anti-interference strategy is dynamically adjusted according to the monitoring result. The survivability and task reliability of the UAV in a long-term, non-steady and complex electromagnetic environment are improved.

Description

Technical Field

[0001] This application relates to the field of anti-interference technology, and more specifically, to a method and system for anti-electromagnetic interference of unmanned aerial vehicles (UAVs). Background Technology

[0002] In an increasingly complex electromagnetic environment, unmanned aerial vehicle (UAV) systems, especially fixed-wing UAVs, face increasingly severe interference threats. Modern cities, industrial areas, military exercise zones, and areas surrounding critical infrastructure are densely populated with various electromagnetic radiation sources, including but not limited to communication base stations (4G / 5G), radio broadcasting, radar systems, high-voltage power lines, power electronic equipment, and various intentional interference sources (such as suppression jammers and spectrum attack devices). These complex and variable electromagnetic signals can form high-intensity, high-density radiation fields across a wide frequency band, posing multiple threats to the navigation, communication, and flight control systems of UAVs. Navigation systems are susceptible to deceptive interference or satellite signal blockage; communication links may be interrupted or hijacked in strong electromagnetic noise; and flight control computers and sensors may malfunction, experience data jumps, or even crash due to electromagnetic pulses or coupling interference.

[0003] Fixed-wing drones are particularly vulnerable in such environments due to their inherent aerodynamic and control characteristics. Compared to multi-rotor drones, fixed-wing platforms typically have higher cruising speeds, rely on aerodynamic control surfaces for attitude and heading control, and often lack hovering capabilities. Once electromagnetic interference causes navigation information to become inaccurate, control commands to be interrupted, or the flight control system to malfunction, the correction window is extremely short, making it easy for them to quickly enter an uncontrollable state, leading to irreversible accidents such as crashes or collisions with obstacles. This results in damage to the aircraft, mission failure, and even secondary safety risks, with costs significantly higher than for multi-rotor drones.

[0004] Existing technological approaches often have limitations in various aspects regarding anti-interference measures. While physical shielding (such as adding shielding covers) can block radiative coupling to some extent, it significantly increases platform weight, alters aerodynamic shape, sacrifices payload and endurance, and is costly. It is effective against low-intensity, conventional frequency band communication interference, but its protective effect drops drastically when facing high-power, wide-spectrum suppressive interference or intelligent spectrum attacks with cognitive and adaptive capabilities. Furthermore, while using multi-sensor redundancy backup (such as combining GPS / INS, visual navigation, and celestial navigation) can improve system robustness, it also significantly increases system complexity and cost. In strong electromagnetic field environments, some sensors (such as magnetic compasses and certain microelectronic inertial measurement units) may also be affected by interference, leading to failure or performance degradation, causing the backup mechanism to malfunction.

[0005] A major shortcoming of current technology lies in the lack of system-level, whole-machine-level electromagnetic compatibility (EMC) design and dynamic anti-interference strategies. Most existing solutions focus only on single modules or local protection, such as enhancing the anti-interference capabilities of communication radios or shielding flight control computers, failing to comprehensively optimize from the perspectives of the entire aircraft's electromagnetic topology, cable layout, common ground design, spectrum sensing, and collaborative management. Furthermore, they lack the ability to dynamically adjust strategies based on the real-time electromagnetic environment during flight, such as adaptive frequency hopping, power control, mode switching, and autonomous emergency return-to-home, making it difficult for UAVs to effectively respond to sudden or new types of interference. Summary of the Invention

[0006] In view of this, the purpose of this application is to provide a method and system for resisting electromagnetic interference of unmanned aerial vehicles (UAVs) in order to improve the above-mentioned problems existing in the prior art.

[0007] In a first aspect, embodiments of this application provide a method for combating electromagnetic interference in unmanned aerial vehicles (UAVs). The method includes: collecting electromagnetic signal data of the environment through a sensor array deployed on the UAV platform; processing the collected electromagnetic signal data to identify the type of interference and estimate the direction of the interference source; fusing the results of the interference feature analysis step with the current flight status information of the UAV to assess and output the electromagnetic threat level; calling and executing corresponding anti-interference strategies from a strategy library according to the electromagnetic threat level, with the intelligent anti-interference decision and control subsystem and the flight module responding in coordination; and continuously monitoring changes in the electromagnetic environment and the execution effect of the anti-interference strategies, and dynamically adjusting the anti-interference strategies based on the monitoring results.

[0008] In the aforementioned implementation process, a multi-dimensional sensor array is deployed to achieve full-spectrum, three-dimensional situational awareness of the electromagnetic environment. Compared to single-point detection methods, this approach captures more complete spatiotemporal characteristics of interference signals, improving the confidence level of subsequent interference identification and the angular resolution of direction estimation. This overcomes the technical shortcomings of traditional single-sensor systems, such as susceptibility to shielding, sampling blind spots, and incomplete spectrum coverage. Real-time processing of electromagnetic signal data and interference type classification, combined with interference source direction estimation, provides UAVs with threat spatial pointing information. This enables the system to distinguish between different attack modes, such as directed energy interference and broadband jamming interference, providing a basis for subsequent differentiated defense decisions. By fusing electromagnetic threat characteristics with flight status information from multiple sources, the limitations of relying solely on signal strength are avoided, making threat level assessment more aligned with real-world scenarios. Through a hierarchical invocation mechanism of the strategy library and deep collaboration between the protection subsystem and flight module, differentiated measures such as electromagnetic hardening, tactical evasion, or mission reconfiguration can be automatically matched based on the threat level, minimizing the impact on mission execution while ensuring flight safety. By assessing anti-jamming effectiveness in real time and triggering strategy updates, the long-term survivability and mission success rate of UAVs in non-stationary electromagnetic threat environments are ensured.

[0009] Optionally, the step of processing the collected electromagnetic signal data to identify the interference type and estimate the direction of the interference source includes: performing time-frequency analysis on the electromagnetic signal data to extract signal features; obtaining the interference type based on the extracted signal features; wherein the interference type includes: natural interference, unintentional interference, and malicious interference; and if the interference type is malicious interference, classifying the malicious interference into at least one of continuous wave interference, pulse interference, or frequency sweep interference.

[0010] In the above implementation process, by performing time-frequency joint domain processing on electromagnetic signal data, the limitations of traditional single-dimensional analysis are overcome. Time-frequency analysis can simultaneously capture the instantaneous frequency characteristics and temporal evolution patterns of interference signals, improving the detection capability and feature separability of weak signals in complex electromagnetic environments. By extracting physically meaningful discriminative features from time-frequency representations, an information compression channel from raw data to high-order semantics is established, which can filter out environmental noise and irrelevant signal components, retain key parameters sensitive to interference types, and reduce the computational complexity and risk of misjudgment of the identification algorithm. The three-level classification step for interference types avoids the ineffective consumption of defense resources and improves the system's focus on interference response. By further identifying specific patterns such as continuous wave interference, pulse interference, or frequency sweep interference under the malicious interference category, the system can understand the attacker's technical structure, providing technical input for the strategy library to call targeted countermeasures, and improving the matching accuracy and suppression effectiveness of anti-interference strategies.

[0011] Optionally, the step of performing time-frequency analysis on the electromagnetic signal data to extract signal features includes: performing a short-time Fourier transform on the electromagnetic signal data to obtain the time-frequency distribution of the signal; extracting the spectral features of the signal based on the time-frequency distribution; calculating the frequency gradient of the power and its change within the observation time window based on the spectral features of the signal; determining the specific type of malicious interference based on the frequency gradient and change of the power using an interference classification function; and calculating the direction angle of arrival of the interference source using the phase difference, signal wavelength, and sensor spacing of the signal received by the sensor array.

[0012] In the above implementation process, short-time Fourier transform is used to perform time-frequency analysis on electromagnetic signals. This not only allows for the simultaneous characterization of interference evolution in both the time and frequency domains but also improves the probability of capturing weak transient features. Furthermore, based on the obtained time-frequency distribution, spectral features are extracted. By calculating the frequency gradient of power and its dynamic changes within the observation window, the system can detect the migration trend of interference forms in advance, thereby transforming post-event response into pre-event warning and effectively compressing the duration of malicious events. After introducing an interference classification function, the accuracy and recall of various interference types are simultaneously improved, reducing resource waste caused by misjudgment. Finally, the combined phase difference, wavelength, and geometric parameters of the sensor array are used to estimate the direction angle of arrival, laying the foundation for subsequent localization and adaptive suppression.

[0013] Optionally, the step of integrating the results of the interference feature analysis step with the current flight status information of the UAV to assess and output the electromagnetic threat level includes: calculating a comprehensive threat index based on the strength, duration, and overlap with the UAV's communication and navigation frequency bands of the interference signal; calculating a flight status impact factor by combining the UAV's flight altitude, speed, and mission phase information; and determining the final electromagnetic threat level based on the comprehensive threat index and the flight status impact factor.

[0014] In the above implementation process, by fusing interference characteristics and real-time flight status, the system uses a comprehensive threat index to uniformly quantify the electromagnetic environment, significantly reducing the coupling uncertainty between multi-source heterogeneous information. By introducing three-dimensional parameters of interference intensity, duration, and frequency band overlap, the system can suppress transient jitter while ensuring assessment sensitivity, achieving a smooth transition of threat levels. Furthermore, by combining altitude, speed, and mission phase to dynamically calculate flight status influencing factors, the assessment results are matched with the UAV, and the final output electromagnetic threat level is both timely and interpretable. This provides a highly reliable decision-making basis for setting trigger thresholds for subsequent adaptive anti-interference strategies, and overall improves the platform's survivability and mission continuity in complex electromagnetic environments.

[0015] Optionally, the threat level includes at least a mild interference level, a moderate interference level, a severe interference level, and an extreme interference level; the step of calling and executing corresponding anti-interference strategies from the strategy library according to the electromagnetic threat level, with the intelligent anti-interference decision and control subsystem and flight module coordinating the response, includes: for the mild interference level, enabling adaptive filtering and enhancing multi-source navigation data fusion; for the moderate interference level, activating a communication parameter agility mechanism and switching to a backup communication link, while adopting a conservative flight control law; and for the severe and extreme interference levels, triggering an autonomous survival emergency protocol, suspending unnecessary signal transmission, and executing a preset emergency flight route based on the redundant navigation system.

[0016] In the above implementation process, by subdividing threats into mild, moderate, severe, and extreme interference levels, a response gradient corresponding to each vulnerability can be established in the policy space, avoiding energy and mission losses caused by overreaction. The adaptive filtering and multi-source fusion enabled at the mild level can suppress navigation errors to the centimeter level without changing the hardware link, ensuring mission continuity. The communication agility and link switching mechanism introduced at the moderate level improves anti-interception and anti-tracking capabilities. The autonomous survival emergency protocol triggered at severe and above levels ensures the platform's safe evacuation along a preset emergency route through silent and redundant navigation coupling, maximizing the preservation of asset value and data integrity.

[0017] Optionally, the continuous monitoring of electromagnetic environment changes and the effectiveness of anti-interference strategies, and the dynamic adjustment of anti-interference strategies based on monitoring results, includes: automatically upgrading the ongoing anti-interference strategy to a strategy corresponding to a higher threat level when interference is detected as not being alleviated; gradually downgrading the anti-interference strategy until normal flight mode is restored when interference is detected as weakening or disappearing; and recording interference events and system response data to iteratively optimize the strategy library.

[0018] In the above implementation process, a closed-loop monitoring mechanism is used to continuously compare the electromagnetic environment and strategy effectiveness. When the interference is not alleviated, the strategy upgrade can be automatically triggered, shortening the manual decision-making link and reducing the risk of loss of control due to response lag. When the interference weakens or disappears, the system will gradually downgrade and restore the normal flight mode, which avoids the unnecessary consumption of high-power anti-interference measures and reduces the secondary inhibition of normal communication and navigation. The interference events and response data recorded throughout the process can be back-optimized by offline mining to optimize the distribution of strategy library parameters, making the next threat identification and strategy matching more accurate. This will continuously improve the robustness and mission adaptability of the UAV in complex electromagnetic environments throughout its entire life cycle.

[0019] Secondly, embodiments of this application provide an anti-electromagnetic interference system for unmanned aerial vehicles (UAVs). The system includes: an electromagnetic environment perception subsystem, an intelligent anti-interference decision-making and control subsystem, and a composite electromagnetic protection subsystem. The electromagnetic environment perception subsystem is used to collect electromagnetic signal data of the environment. The intelligent anti-interference decision-making and control subsystem is connected to the electromagnetic environment perception subsystem and is used to perform interference feature analysis, threat assessment, strategy matching, and control command generation. The composite electromagnetic protection subsystem is connected to the intelligent anti-interference decision-making and control subsystem and is used to receive the control commands and perform corresponding physical layer and signal layer protection actions.

[0020] In the above implementation process, the electromagnetic environment perception subsystem captures the spatial electromagnetic distribution in real time with a high-sensitivity array front end, providing low-latency, high-fidelity raw data for subsequent decision-making; the intelligent anti-interference decision and control subsystem integrates interference characteristics and flight status to quickly complete threat assessment and strategy matching, significantly reducing the frequency of manual intervention; the composite electromagnetic protection subsystem executes physical layer and signal layer actions synchronously according to control commands, realizing three-dimensional protection through software and hardware collaboration and space-frequency integration, effectively improving the platform's survivability and mission continuity in complex electromagnetic environments, and reserving a plug-and-play interface framework for subsequent functional upgrades.

[0021] Optionally, the electromagnetic environment sensing subsystem includes: a multi-band field strength monitoring array, an interference feature analysis module, and a source orientation module; the multi-band field strength monitoring array is distributed on the wings and fuselage of the UAV; the interference feature analysis module is used to analyze the monitoring data to identify the type of interference; and the source orientation module is used to estimate the direction of the interference source based on the data from the monitoring array.

[0022] In the above implementation process, the multi-band field strength monitoring array is embedded in the wing and fuselage in a distributed manner, which can achieve this without changing the aerodynamic shape. The airspace coverage reduces detection blind spots caused by airframe obstruction; the interference feature analysis module performs real-time analysis of monitoring data, which can quickly separate transient and continuous interference in complex backgrounds, improve identification accuracy and reduce front-end transmission bandwidth; the source orientation module uses array phase consistency to estimate the direction of incoming waves, and provides highly reliable angle input for directional interference suppression, giving UAVs an early airborne advantage in electromagnetic countermeasures environments.

[0023] Optionally, the intelligent anti-interference decision and control subsystem includes: a fusion decision unit and a policy library executor; the fusion decision unit is used to fuse interference characteristics and flight status information to assess the threat level; the policy library executor is used to invoke pre-stored anti-interference policies and generate control commands according to the threat level.

[0024] In the above implementation process, the fusion decision-maker couples and evaluates interference features with flight status information through a unified framework, which can generate low-redundancy and high-sensitivity threat levels in a high-dimensional parameter space, significantly reducing the probability of misjudgment caused by the uncertainty of a single information source; the policy library executor uses the threat level as an index to call pre-stored policies and automatically generate control commands, avoiding the delay risk caused by manual intervention; the collaboration of the two enables the system to have interpretable and scalable decision-making capabilities, providing a standardized interface for subsequent policy iteration and function upgrades, and improving the autonomous survival and mission continuity of UAVs in complex electromagnetic environments.

[0025] Optionally, the composite electromagnetic protection subsystem includes: an electromagnetic shielding structure and an adaptive filtering and agile communication module; the electromagnetic shielding structure is used to provide an independent physical shielding space for the core flight control and navigation equipment of the UAV; the adaptive filtering and agile communication module is used to dynamically adjust the filtering parameters and transmission frequency of the communication signal according to the control command.

[0026] In the above implementation process, the electromagnetic shielding structure constructs an independent physical barrier for the core flight control and navigation equipment. Without increasing the overall weight budget, it can attenuate the external radiation field strength to below the safety threshold, significantly reducing the probability of transient false triggering of critical circuits. The adaptive filtering and agile communication modules dynamically adjust the filtering parameters and transmission frequency points at the millisecond level according to the control commands, effectively compressing the interference lock-in window. The hardware and software collaboration of the two can simultaneously construct three-dimensional protection at the physical and signal layers, ensuring that the platform maintains the telemetry and control link and navigation accuracy under continuous electromagnetic suppression, and improving the overall mission reliability and asset survivability in complex electromagnetic environments.

[0027] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores program instructions, and when the processor reads and runs the program instructions, it executes the steps in any of the above implementation methods.

[0028] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the steps in any of the above implementations. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A first flowchart of an anti-electromagnetic interference method for unmanned aerial vehicles provided in this application embodiment; Figure 2 A second flowchart of an anti-electromagnetic interference method for unmanned aerial vehicles provided in this application embodiment; Figure 3 The third flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in the embodiments of this application; Figure 4 The fourth flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in the embodiments of this application; Figure 5The fifth flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in this application embodiment; Figure 6 The sixth flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in this application embodiment; Figure 7 A simplified schematic diagram of an anti-electromagnetic interference system for unmanned aerial vehicles provided in this application embodiment; Figure 8 Working diagram for interference feature analysis provided in the embodiments of this application; Figure 9 Threat level assessment logic diagram provided for embodiments of this application; Figure 10 This is a schematic diagram of the hardware structure layout provided in the embodiments of this application; Figure 11 This is a block diagram of an electronic device provided in an embodiment of this application.

[0031] Icons: 010-Electromagnetic Environment Sensing Subsystem; 011-Multi-band Field Strength Monitoring Array; 012-Interference Feature Analysis Module; 013-Source Direction Module; 020-Intelligent Anti-interference Decision and Control Subsystem; 021-Fusion Decision Controller; 022-Strategy Library Executor; 030-Composite Electromagnetic Protection Subsystem; 031-Electromagnetic Shielding Structure; 032-Adaptive Filtering and Agile Communication Module; 100-Electronic Equipment; 111-Memory; 112-Memory Controller; 113-Processor; 114-Peripheral Interface; 115-Input / Output Unit; 116-Display Unit. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0033] In a first aspect, embodiments of this application provide a method for resisting electromagnetic interference in unmanned aerial vehicles (UAVs), which is applied to a server. The server can be an electronic device with logic computing functions, such as a personal computer (PC), tablet computer, smartphone, or personal digital assistant (PDA), or an anti-electromagnetic interference system for UAVs.

[0034] Please see Figure 1 , Figure 1 The first flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in the embodiments of this application is shown.

[0035] The anti-electromagnetic interference method for UAVs includes: collecting electromagnetic signal data of the environment through a sensor array deployed on the UAV platform; processing the collected electromagnetic signal data to identify the type of interference and estimate the direction of the interference source; fusing the results of the interference feature analysis step with the current flight status information of the UAV to assess and output the electromagnetic threat level; calling and executing the corresponding anti-interference strategy from the strategy library according to the electromagnetic threat level, with the control module and flight module responding in coordination; and continuously monitoring changes in the electromagnetic environment and the execution effect of the anti-interference strategy, and dynamically adjusting the anti-interference strategy based on the monitoring results.

[0036] In the aforementioned implementation process, environmental electromagnetic signal data was collected by a sensor array deployed on the UAV platform, enabling real-time, all-around perception of the surrounding electromagnetic environment and providing a rich and accurate data foundation for subsequent analysis. Furthermore, the collected data was processed to identify interference types and estimate the direction of interference sources, improving both the accuracy and speed of interference identification and providing crucial spatial information for targeted responses. Based on this, threat assessment was further performed by integrating interference characteristics with the UAV's current flight status, making threat level determination more scientific and aligned with actual mission scenarios, effectively avoiding overreaction or underreaction. Corresponding anti-interference strategies were retrieved from the strategy library and executed according to the threat level, driving the protection subsystem and flight control module to respond collaboratively. This ensured the timeliness of measures and enhanced the overall coordination and adaptability of the system. Finally, by continuously monitoring changes in the electromagnetic environment and the effectiveness of strategy execution, and dynamically adjusting anti-interference strategies, the long-term survivability and mission reliability of the UAV in time-varying electromagnetic environments were improved.

[0037] Optionally, please refer to Figure 2 , Figure 2 The second flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in the embodiments of this application is shown.

[0038] The process involves processing the collected electromagnetic signal data to identify the type of interference and estimate the direction of the interference source, including: performing time-frequency analysis on the electromagnetic signal data to extract signal features; obtaining the type of interference based on the extracted signal features; wherein the types of interference include: natural interference, unintentional interference, and malicious interference; and, in the case of malicious interference, classifying the malicious interference into at least one of continuous wave interference, pulse interference, or frequency sweep interference.

[0039] In the aforementioned implementation process, by performing time-frequency analysis and extracting features from electromagnetic signal data, the transient and steady-state characteristics of signals can be captured in complex electromagnetic environments where the time and frequency domains are intertwined, improving the reliability and accuracy of the analysis. Secondly, based on the extracted features, interference types are clearly classified into natural interference, unintentional interference, and malicious interference, achieving a fundamental distinction between the source and intent of interference. This allows for prioritizing threatening artificial interference, improving the efficiency of resource allocation and the targeted nature of decision-making. Furthermore, after determining it to be malicious interference, it is further subdivided into specific categories such as continuous wave interference, pulse interference, or frequency sweep interference. This not only deepens the understanding of interference methods and technical characteristics but also provides direct and crucial technical parameters for calling highly matched countermeasures in the strategy library.

[0040] Optionally, please refer to Figure 3 , Figure 3 The third flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in the embodiments of this application is shown.

[0041] Time-frequency analysis is performed on electromagnetic signal data to extract signal features, including: performing short-time Fourier transform on the electromagnetic signal data to obtain the time-frequency distribution of the signal; extracting the spectral features of the signal based on the time-frequency distribution; calculating the frequency gradient of the power and its change within the observation time window based on the spectral features of the signal; determining the specific type of malicious interference based on the frequency gradient and change of the power through an interference classification function; and calculating the direction angle of arrival of the interference source by using the phase difference of the signal received by the sensor array, the signal wavelength, and the sensor spacing.

[0042] In the above implementation process, the short-time Fourier transform (SFT) acquires the time-frequency distribution of the signal, effectively overcoming the limitations of traditional frequency or time domain analysis in processing non-stationary signals. It can simultaneously characterize the dynamic characteristics of interference signals in two-dimensional space (time and frequency), providing a high-resolution joint domain representation for feature extraction. Based on the time-frequency distribution, spectral features are extracted, and the frequency gradient of power and its time-varying characteristics are further calculated. This transforms the intuitive time-frequency image into quantifiable, physically meaningful feature parameters. These parameters clearly reflect the essential differences between different types of malicious interference, thus laying a reliable data foundation for subsequent accurate classification. The interference classification function makes judgments based on these features, achieving automated and rapid mapping from data to decision, improving the system's ability to identify threat types in real time in complex electromagnetic environments. Simultaneously, the direction angle of arrival of the interference source is calculated using the phase difference, signal wavelength, and spacing information of the sensor array. This combines signal processing with spatial geometry, enabling directional estimation of the interference source and providing crucial spatial pointing information for possible subsequent evasion or countermeasures.

[0043] Optionally, please refer to Figure 4 , Figure 4The fourth flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in the embodiments of this application is shown.

[0044] The results of the interference feature analysis steps are combined with the current flight status information of the UAV to assess and output the electromagnetic threat level, including: calculating the comprehensive threat index based on the strength, duration and overlap of the interference signal with the UAV's communication and navigation frequency bands; calculating the flight status impact factor by combining the UAV's flight altitude, speed and mission phase information; and determining the final electromagnetic threat level based on the comprehensive threat index and the flight status impact factor.

[0045] In the aforementioned implementation process, the comprehensive threat index is calculated by considering the strength and duration of the interference signal, as well as its overlap with communication and navigation frequency bands. This achieves an objective quantification of the physical characteristics and potential hazards of the interference itself, ensuring the scientific accuracy of the assessment's technical benchmark. The introduction of flight state influencing factors incorporates dynamic contexts such as the UAV's current altitude, speed, and mission stage into the assessment system. This fully recognizes that the actual impact of the same interference can vary drastically under different flight states, thus moving threat assessment away from static and isolated analysis. Finally, the comprehensive threat index and flight state influencing factors are weighted or fused using rules to determine the final level. This not only avoids misjudgments caused by ignoring the flight context but also provides highly refined and scenario-based threat inputs for the subsequent strategy library. This ensures that the invoked anti-interference strategies effectively address electromagnetic threats while minimizing the impact on the UAV's intended flight mission, thereby achieving an optimal balance between safety, efficiency, and mission completion capability.

[0046] Optionally, please refer to Figure 5 , Figure 5 The fifth flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in this application embodiment.

[0047] The threat levels include at least mild, moderate, severe, and extreme interference levels. Based on the electromagnetic threat level, the corresponding anti-interference strategy is invoked and executed from the strategy library, and the control module and flight module respond in coordination. This includes: for mild interference levels, enabling adaptive filtering and enhancing multi-source navigation data fusion; for moderate interference levels, activating the communication parameter agility mechanism and switching to the backup communication link, while adopting a conservative flight control law; and for severe and extreme interference levels, triggering the autonomous survival emergency protocol, suspending unnecessary signal transmission, and executing a preset emergency flight route based on the redundant navigation system.

[0048] In the aforementioned implementation process, four progressive threat levels—mild, moderate, severe, and extreme—were clearly defined, enabling differentiated responses based on the severity of the interference. Specific and actionable strategies were pre-set for each level, constructing a clearly hierarchical and progressively escalating response ladder. For mild interference, software optimization techniques such as adaptive filtering and enhanced data fusion were employed, barely altering the established flight status and mission flow, reflecting the principles of lowest cost and minimal impact on mission continuity. For moderate interference, the strategy was upgraded to proactively intervene at the hardware and basic control layers, dynamically reconstructing its own communication characteristics and reducing maneuverability to ensure core link security and flight stability, achieving a critical balance between countering interference and maintaining the mission. For severe and extreme interference, the highest level of self-protection mode was activated, aiming to maximize platform safety, conceal itself, and escape the threat area under extremely harsh electromagnetic environments.

[0049] Optionally, please refer to Figure 6 , Figure 6 The sixth flowchart of the anti-electromagnetic interference method for unmanned aerial vehicles provided in this application embodiment.

[0050] Continuously monitor changes in the electromagnetic environment and the effectiveness of anti-interference strategies, and dynamically adjust anti-interference strategies based on monitoring results. This includes: automatically upgrading the ongoing anti-interference strategy to the strategy corresponding to a higher threat level if interference is detected as not being mitigated; gradually downgrading the anti-interference strategy until normal flight mode is restored if interference is detected as weakening or disappearing; and recording interference events and system response data to iteratively optimize the strategy library.

[0051] In the aforementioned implementation process, by monitoring the strategy execution effect in real time and implementing bidirectional dynamic adjustments, the strategy is automatically upgraded when interference is detected as unresolved. This ensures that the response strength keeps pace with the urgency of threat evolution, effectively avoiding the risk of response failure due to insufficient initial strategy, and enhancing the system's robustness and reliability. Conversely, by gradually downgrading the strategy until normalcy is restored when interference weakens, the system demonstrates its economy and task orientation. It can release occupied system resources in a timely manner while ensuring safety, quickly returning to the optimal task execution state and maximizing task efficiency. Based on the data recording and iterative optimization of the strategy library for interference events and responses, the effectiveness of existing strategies can be continuously verified and corrected through systematic recording and analysis. New interference patterns and response rules can even be discovered. This allows the entire system's strategy library to break free from the limitations of static presets and continuously evolve with the accumulation of task experience. The anti-interference knowledge base is continuously enriched and improved, thereby enhancing the overall adaptability and intelligence of UAVs when facing new or complex combinations of interference in the future.

[0052] Secondly, this application provides an anti-electromagnetic interference system for unmanned aerial vehicles (UAVs). Please refer to [link to relevant documentation]. Figure 7 , Figure 7 This is a simplified schematic diagram of an anti-electromagnetic interference system for unmanned aerial vehicles (UAVs) provided in an embodiment of this application.

[0053] The UAV anti-electromagnetic interference system includes: an electromagnetic environment perception subsystem 010, an intelligent anti-interference decision and control subsystem 020, and a composite electromagnetic protection subsystem 030; the electromagnetic environment perception subsystem 010 is used to collect electromagnetic signal data of the environment; the intelligent anti-interference decision and control subsystem 020 is connected to the electromagnetic environment perception subsystem 010 and is used to perform interference characteristic analysis, threat assessment, strategy matching, and control command generation; the composite electromagnetic protection subsystem 030 is connected to the intelligent anti-interference decision and control subsystem 020 and is used to receive control commands and perform corresponding physical layer and signal layer protection actions.

[0054] In the above implementation process, the functional decoupling and collaborative division of labor between subsystems ensure the system's high efficiency and reliability. The electromagnetic environment sensing subsystem 010 specializes in high-fidelity, wide-band electromagnetic signal data acquisition, providing a clean and reliable data source for subsequent analysis. The intelligent anti-interference decision-making and control subsystem 020 integrates core intelligent algorithms such as signal processing, threat assessment, strategy matching, and command generation, realizing the intelligent transformation from raw data to control commands. The composite electromagnetic protection subsystem 030 is responsible for executing specific protection actions at the physical and signal layers. Information flows from the sensing subsystem to the decision-making subsystem, and the control commands generated after intelligent analysis flow to the protection subsystem for execution, ensuring that the system can respond quickly and orderly to real-time changes in the electromagnetic environment. As the core hub, the intelligent anti-interference decision-making and control subsystem 020 not only processes signals and data at the software level but also coordinates and schedules hardware resources in the composite electromagnetic protection subsystem 030, achieving seamless connection and collaborative optimization from algorithmic decision-making to physical anti-interference actions.

[0055] Optionally, the electromagnetic environment sensing subsystem 010 includes: a multi-band field strength monitoring array 011, an interference feature analysis module 012, and a source orientation module 013; the multi-band field strength monitoring array 011 is distributed on the wings and fuselage of the UAV; the interference feature analysis module 012 is used to analyze the monitoring data to identify the type of interference; and the source orientation module 013 is used to estimate the direction of the interference source based on the data from the monitoring array.

[0056] In the above implementation process, the multi-band field strength monitoring array 011 is distributed across the wings and fuselage, fully utilizing the UAV's spatial structure to achieve high spatial resolution monitoring of the omnidirectional electromagnetic environment, effectively avoiding perception blind spots. Distributed deployment enhances the sensitivity and reliability of detecting weak signals and directional interference through the fusion and comparison of data from multiple nodes, laying a solid foundation of raw data for subsequent interference identification and localization. The core analysis function is broken down into an interference feature analysis module 012 and a source orientation module 013, achieving specialization and parallelization of the data processing flow. The interference feature analysis module 012 focuses on rapidly extracting and classifying interference features from the time and frequency domains. Meanwhile, the source orientation module 013 specifically utilizes the array's geometric configuration and signal phase information to estimate the direction of arrival (DOA).

[0057] Optionally, the intelligent anti-interference decision and control subsystem 020 includes: a fusion decision unit 021 and a strategy library executor 022; the fusion decision unit 021 is used to fuse interference characteristics and flight status information to assess the threat level; the strategy library executor 022 is used to invoke pre-stored anti-interference strategies according to the threat level and generate control commands.

[0058] In the above implementation process, the dynamic threat assessment function and the deterministic policy execution function are separated, and handled by the fusion decision-maker 021 and the policy library executor 022 respectively. This allows the threat judgment algorithm and the response policy library to be developed, tested, and updated independently, improving the system's maintainability and scalability. The core function of the fusion decision-maker 021 is that it does not rely solely on the physical characteristics of electromagnetic interference for judgment, but rather integrates them with the UAV's real-time flight status for comprehensive calculation. This ensures that the threat level reflects the actual impact of the interference in the current specific mission context, providing a crucial basis for taking the most appropriate response measures. The policy library executor 022 operates based on a pre-generated and verified policy mapping table. Once a threat level is received, it can generate deterministic control commands without delay. This design ensures the consistency and reliability of the system response under stringent real-time requirements, avoiding the latency or uncertainty that may arise from complex online calculations.

[0059] Optionally, the composite electromagnetic protection subsystem 030 includes: an electromagnetic shielding structure 031 and an adaptive filtering and agile communication module 032; the electromagnetic shielding structure 031 is used to provide an independent physical shielding space for the core flight control and navigation equipment of the UAV; the adaptive filtering and agile communication module 032 is used to dynamically adjust the filtering parameters and transmission frequency of the communication signal according to the control command.

[0060] In the aforementioned implementation process, the electromagnetic shielding structure 031 provides a physical isolation barrier for core equipment such as flight control and navigation. It can deterministically attenuate the intensity of external electromagnetic fields in specific frequency bands, serving as a reliable basic defense against broadband, high-intensity interference and ensuring a stable working environment for the UAV at the most basic hardware level. The adaptive filtering and agile communication module 032, based on control commands issued by the intelligent decision-making subsystem, can dynamically adjust the signal processing and communication links, enabling the system to implement rapid and precise countermeasures against specific types of interference, thereby maintaining effective communication and navigation capabilities in complex electromagnetic interactions.

[0061] The following description, in conjunction with the above figures, illustrates a specific embodiment of this application.

[0062] In a specific embodiment of this application, the electromagnetic environment perception subsystem 010 consists of a multi-band field strength monitoring array 011, an interference feature analysis module 012, and a source orientation module 013 deployed on the wings and fuselage of the UAV. It is used to collect and analyze electromagnetic environment data in real time to identify the type of interference and estimate the direction of the interference source. The composite electromagnetic protection subsystem 030 includes a distributed shielding layer, an electromagnetic fortress, and an adaptive filtering and agile communication module 032. The electromagnetic fortress provides independent shielding protection for the flight control computer, inertial navigation module, and satellite navigation receiver. The adaptive filtering and agile communication module 032 dynamically adjusts communication parameters according to decision commands. The intelligent anti-interference decision and control subsystem 020 is equipped with a fusion decision unit 021 and a strategy library executor 022. It can comprehensively assess the threat level by combining perception data and flight status information, and automatically call preset anti-interference strategies to control the protection subsystem and flight module to respond in coordination, realizing full-process anti-interference management from monitoring and decision-making to protection. Electromagnetic environment data is collected in real time by a multi-band field strength monitoring array 011 deployed on the wings and fuselage of the UAV. The monitoring frequency bands include at least the GPS band, remote control band, and data link band. The sensors adopt a high-sensitivity wide-band design with a sampling rate of no less than 1MHz and continuously upload the field strength data to the interference feature analysis module 012. The interference feature analysis module 012 performs time-frequency analysis on the real-time collected electromagnetic signals, distinguishes between natural interference, unintentional interference, and malicious interference by comparing signal characteristics, and further identifies the specific forms of malicious interference, including continuous wave interference, pulse interference, or frequency sweeping interference. At the same time, it combines the source orientation module 013 with the sensor array Phase difference calculation determines the direction angle of arrival of strong interference sources, generating an interference characteristic report. Time-frequency analysis is used to identify interference types by extracting signal features based on the signal's spectral and time-domain characteristics. The acquired signals undergo time-frequency transformation, and feature comparison distinguishes between natural, unintentional, and malicious interference. Malicious interference is further categorized into continuous wave interference, pulse interference, or frequency sweep interference. Therefore, this step combines Short-Time Fourier Transform (STFT), spectral feature classification functions, and sensor array phase difference calculation technology to construct a multi-dimensional interference identification and localization system. This system accurately distinguishes interference types and locates the direction of interference sources, as detailed below: Please see Figure 8 , Figure 8 This is a working diagram for interference feature analysis provided in the embodiments of this application.

[0063] For time-frequency analysis, Short-Time Fourier Transform (STFT) is used to decompose the electromagnetic signal in the time and frequency domain. Through STFT transformation, the local characteristics of the signal at different times and frequencies are obtained, which provides a basis for distinguishing interference types. Let the input signal be... Its STFT result is The STFT calculation formula is as follows:

[0064] in, For window functions, For frequency, For time, Using the integral variable, the frequency components of the signal change over time by analyzing the time-frequency results of the STFT, thereby identifying whether a specific type of interference exists; To further differentiate the specific types of malicious interference, it is classified according to the spectral characteristics of the signal. Continuous wave interference is characterized by a strong signal at a single frequency point; pulse interference exhibits a strong power jump within a short period and a wide spectral width; and swept-frequency interference displays a gradual change in signal power over a relatively wide frequency band. To more accurately determine these forms of interference, an interference classification function based on spectral characteristics is used. The type of interference can be determined by analyzing the frequency distribution, power change rate, and duration of the signal using functions.

[0065] in, It is the signal at frequency and time On the power, It is the observation time window. It is the frequency gradient of power. By calculating the frequency gradient of signal power and combining it with the trend of change within a time window, the specific type of interference can be effectively identified. To accurately determine the direction of the interference source, the direction angle of arrival of the strong interference source is estimated by calculating the phase difference of the sensor array, using the source orientation module 013. Let the phase difference of the sensor array be... The wavelength of the signal propagation is The spacing between the sensor arrays is Incoming wave direction angle It can be calculated using the following formula:

[0066] The above formula uses the phase difference of the signals received by the array sensors, combined with the known propagation wavelength and the sensor spacing, to calculate the azimuth angle of the interference source. This provides a basis for subsequent interference source location and protection decisions; Through the combined application of these time-frequency analysis and source orientation algorithms, the interference feature analysis module 012 can accurately identify the type of interference from the acquired electromagnetic signals, determine the location information of the interference source, and finally generate a detailed interference feature report.

[0067] Please see Figure 9 , Figure 9The threat level assessment logic diagram provided for the embodiments of this application.

[0068] The intelligent anti-interference decision subsystem receives interference characteristic reports and integrates current flight status parameters, including flight altitude, speed and mission phase information. It generates an electromagnetic threat level through a preset threat assessment algorithm. The threat level is divided into four levels: mild interference, moderate interference, severe interference and extreme interference. The threat level assessment is based on factors such as interference signal strength, duration, frequency band overlap and actual impact on the navigation and communication system. The strength of the interference signal Duration Frequency band overlap This is a key parameter affecting threat level. (Interference signal strength) This represents the power ratio of the interfering signal relative to the background noise, while the duration... This measures the duration of interference signals and the degree of frequency band overlap. This indicates the degree of overlap between the frequency band occupied by the interfering signal and the frequency band of the aircraft's communication and navigation signals. To quantify the threat posed by these factors to the aircraft, a comprehensive threat index is defined. The calculation formula is as follows:

[0069] in, , , These are weighting coefficients, corresponding to the relative importance of interference intensity, duration, and frequency band overlap, respectively. This formula quantifies each interference feature into a comprehensive threat index through a weighted summation. It is used to assess the degree of threat that interference poses to aircraft safety; Considering the current flight status parameters of the aircraft, flight altitude and flight speed The impact on threat levels is mainly reflected in the aircraft's anti-jamming capabilities and sensitivity to external interference. It is assumed that the aircraft is more sensitive to interference when flying at lower altitudes or speeds, and vice versa. (Mission phase information) This also needs to be included in the evaluation, during the task phase. This reflects the criticality of the aircraft's mission; for example, the impact of interference may be more severe when performing important tasks or in critical phases. To incorporate flight status parameters into threat assessment, a comprehensive flight status impact factor is defined. This factor is evaluated by combining flight altitude, speed, and mission phase, and the calculation formula is as follows:

[0070] in, , , These are the corresponding weighting coefficients, reflecting the contribution of each flight status parameter to threat assessment, combined with... and To obtain the final threat level. The calculation formula is as follows:

[0071] Through this comprehensive calculation, the following was obtained: The system classifies interference threats into four levels: mild interference, moderate interference, severe interference, and extreme interference. Mild interference indicates that the interference signal has little impact on the aircraft's navigation and communication, and the system can operate normally. Moderate interference indicates that the interference signal may affect some parts of the system, but not to the point of complete failure. Severe interference means that the interference signal seriously affects the normal operation of the aircraft, requiring emergency measures. Extreme interference indicates that the intensity and impact of the interference signal are extremely high, and the aircraft must take immediate emergency control measures.

[0072] Based on the threat level, the system matches and executes anti-jamming strategies pre-stored in the strategy library, which contains four core response schemes: for mild interference, it activates an adaptive filter and strengthens the fusion and verification of multi-source navigation data; for moderate interference, it activates a communication frequency switching mechanism and switches to a backup antenna while the flight control adopts a conservative control law; for severe interference, it triggers an autonomous survival emergency protocol to control the suspension of signal transmission and relies on high-precision inertial navigation and visual navigation to maintain the track; and for extreme interference, it executes an autonomous survival emergency protocol to control the UAV to climb to a safe altitude and return or hover along a preset electromagnetic clean route. The intelligent anti-interference decision-making subsystem executes anti-interference strategies pre-stored in the strategy library based on the generated electromagnetic threat level. The system will take different countermeasures for different interference threat levels to ensure the safe operation of the aircraft. The following provides a detailed explanation of each strategy and introduces relevant formulas to quantify the effect of strategy execution.

[0073] For mild interference, when the interference signal strength is low and its impact on the aircraft is limited, the system suppresses the interference by activating an adaptive filter and enhances the fusion verification of multi-source navigation data. The adaptive filter dynamically adjusts the filter gain based on the real-time interference strength, thereby reducing the impact of interference on the navigation signal. Let the interference signal strength be... The filter gain is The gain of the adaptive filter can be calculated using the following formula:

[0074] in, This is the adjustment coefficient of the filter. The time response window of the filter. The formula indicates that as the interference intensity increases, the filter gain also increases, thereby enhancing the suppression effect on the interference signal. At this time, the system further improves navigation accuracy and reduces the impact of interference on flight missions by combining data sources such as GPS, inertial navigation and visual navigation through multi-source navigation data fusion verification. When the interference escalates to moderate interference, the aircraft will activate its communication frequency switching mechanism and switch to a backup antenna to avoid continuous interference signals. Simultaneously, the flight control system will employ a conservative control law to limit the aircraft's range of motion and ensure flight stability. The communication frequency switching time... Based on the duration of the interference and switching frequency The calculation is done using the following formula:

[0075] in, It is the duration of the interference. It refers to the frequency of frequency switching. The purpose of frequency switching is to reduce the impact of interference on communication systems through frequency switching. This reflects the efficiency of frequency switching. After switching to the backup antenna, the flight control system automatically switches to a conservative control law, minimizing the impact on the aircraft under moderate interference conditions. When interference reaches a severe level, the system will trigger an autonomous survival emergency protocol to suspend all signal transmission and rely on high-precision inertial navigation and visual navigation to maintain its trajectory. To quantify the signal pause time... Using interference signal strength With duration Calculate based on the relationship: in, It is the strength of the interference signal. This refers to the duration of the interference. This formula shows that the stronger the interference signal, the longer the system will pause its signal. Through this strategy, the aircraft can maintain a stable trajectory under the influence of interference signals and avoid further interference to the aircraft system. When the interference reaches an extreme level, the aircraft will activate its autonomous survival emergency protocol control, first rapidly climbing to a safe altitude. Then, along the preset electromagnetic cleanroom route Return to base or circle and wait, at a safe altitude. The formula is determined by the aircraft's climb capability and the strength of the current interference signal: in, It is the strength of the interference signal. It refers to the duration of the interference. This formula represents the system's safety adjustment factor. It reflects the need for the aircraft to climb to a higher safe altitude to avoid entering the interference zone as the strength and duration of the interference signal increase. The aircraft will follow a pre-set electromagnetic clean-up route. Perform return-to-base or hovering standby operations to ensure the aircraft operates stably within a safe area.

[0076] The electromagnetic environment sensing subsystem 010 continuously monitors changes in electromagnetic interference to ensure real-time adjustments to response strategies. When interference is detected as not being effectively mitigated, the system automatically upgrades its response strategy, transitioning to a higher threat level. For example, under mild interference, an adaptive filter is activated. If the interference worsens and is not effectively suppressed, the system upgrades to a moderate interference strategy, initiating a communication frequency switching mechanism and switching to a backup antenna. Simultaneously, the flight control system adjusts the control law based on real-time flight status to ensure flight stability. Conversely, when the interference signal gradually weakens or disappears, the system gradually degrades back to normal flight mode to reduce interference with the flight mission. During this process, all interference events and system response data are automatically recorded and stored for post-event analysis and strategy optimization. This data not only provides feedback support for current anti-interference strategies but also provides valuable references for future system iterations and improvements, helping to enhance the system's adaptability and anti-interference capabilities in complex electromagnetic environments. Through such real-time monitoring and dynamic adjustment, the system can flexibly respond to different interference scenarios, ensuring the aircraft always maintains optimal performance.

[0077] In one specific embodiment of this application, please refer to Figure 10 , Figure 10 This is a schematic diagram showing the hardware structure layout provided in the embodiments of this application.

[0078] The hardware layout of the medium-sized fixed-wing UAV is as follows: Multiple broadband electromagnetic sensors are evenly distributed along the spanwise direction of the wing leading edge, and several additional sensors of the same type are deployed at the top of the vertical tail. This multi-point sensor array enables comprehensive, multi-frequency perception of the surrounding electromagnetic environment. A main shielded cabin is set up in the core area of ​​the fuselage, integrating key flight control and communication components such as the flight controller, GNSS receiver, and main data link module into this independent shielded space. This physical isolation reduces the impact of external electromagnetic interference on the core system. A separate auxiliary shielded cabin is set up in the nose area, housing redundant navigation and perception equipment such as backup inertial navigation and vision processing units, serving as backup support when the main system is interfered with. The main and auxiliary shielded cabins communicate via an electromagnetic interference-resistant fiber optic link, ensuring stable transmission of critical information while avoiding the electromagnetic coupling risks associated with cable conduction. The overall layout takes into account the comprehensiveness of electromagnetic environment perception, the anti-interference protection of core components, and the redundancy and reliability of the system.

[0079] Optionally, please refer to Figure 11 , Figure 11 This is a block diagram illustrating an electronic device according to an embodiment of this application. The electronic device 100 may include a memory 111, a memory controller 112, a processor 113, a peripheral interface 114, an input / output unit 115, and a display unit 116. Those skilled in the art will understand that... Figure 11 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device 100. For example, the electronic device 100 may also include components that are more... Figure 11 The more or fewer components shown, or having the same Figure 11 The different configurations shown.

[0080] The aforementioned memory 111, memory controller 112, processor 113, peripheral interface 114, input / output unit 115, and display unit 116 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The aforementioned processor 113 is used to execute executable modules stored in the memory.

[0081] The memory 111 can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 111 stores programs, and the processor 113 executes these programs upon receiving execution instructions. The methods executed by the electronic device 100 as defined in any embodiment of this application can be applied to the processor 113, or implemented by the processor 113.

[0082] The aforementioned processor 113 may be an integrated circuit chip with signal processing capabilities. The processor 113 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor.

[0083] The peripheral interface 114 described above couples various input / output devices to the processor 113 and the memory 111. In some embodiments, the peripheral interface 114, the processor 113, and the memory controller 112 can be implemented on a single chip. In other instances, they can be implemented on separate chips.

[0084] The input / output unit 115 described above is used to provide user input data. The input / output unit 115 may be, but is not limited to, a mouse and keyboard, etc.

[0085] The aforementioned display unit 116 provides an interactive interface (e.g., a user interface) between the electronic device 100 and the user, or displays image data for the user's reference. In this embodiment, the display unit can be a liquid crystal display (LCD) or a touch display. If it is a touch display, it can be a capacitive touchscreen or a resistive touchscreen that supports single-point and multi-point touch operations. Supporting single-point and multi-point touch operations means that the touch display can sense touch operations generated simultaneously from one or more locations on the touch display and pass the sensed touch operations to the processor for calculation and processing.

[0086] This application also provides a computer-readable storage medium storing computer program instructions, which are read and executed by a processor to perform steps in the method for combating electromagnetic interference in unmanned aerial vehicles.

[0087] In summary, based on perceived data and the subsequently constructed decision-making and execution mechanisms, interference feature extraction and source orientation analysis transform raw signals into understandable interference types, patterns, and location intelligence. Threat assessment and level determination, through quantitative algorithms that fuse flight status and interference characteristics, achieve objective and graded threat evaluation. Strategy matching and execution, based on threat levels, precisely trigger tiered response strategies from filtering to emergency return-to-base. Together, these actions elevate single signal monitoring into a complete intelligent decision-making chain from identification and assessment to response, achieving automation, precision, and tiered anti-interference response. By continuously monitoring the effectiveness of strategy execution and environmental changes, online real-time adjustment and adaptive optimization of anti-interference strategies are achieved. Simultaneously, the system records all interference events and response data, providing a data-driven foundation for subsequent strategy library iterations and algorithm optimization. This transforms the entire system from a static response procedure into an intelligent anti-interference ecosystem with online learning and continuous evolution capabilities, significantly improving the survivability and mission reliability of UAVs in long-term, unsteady, and complex electromagnetic environments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed device can also be implemented in other ways. The device embodiments described above are merely illustrative; for example, the block diagrams in the accompanying drawings illustrate the possible architecture, functions, and operations of the device according to various embodiments of this application. In this regard, each block in the block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram, and combinations of block diagrams, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0089] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0090] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0091] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0093] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for resisting electromagnetic interference in unmanned aerial vehicles (UAVs), characterized in that, The method includes: Electromagnetic signal data of the environment are collected by a sensor array deployed on the drone platform; The collected electromagnetic signal data is processed to identify the type of interference and estimate the direction of the interference source; By integrating the results of the aforementioned interference feature analysis steps with the current flight status information of the UAV, the electromagnetic threat level is assessed and output. Based on the electromagnetic threat level, the corresponding anti-interference strategy is called from the strategy library and executed, and the intelligent anti-interference decision and control subsystem and the flight module respond in coordination. It also continuously monitors changes in the electromagnetic environment and the effectiveness of anti-interference strategies, and dynamically adjusts the anti-interference strategies based on the monitoring results.

2. The method according to claim 1, characterized in that, The process of processing the collected electromagnetic signal data to identify the type of interference and estimate the direction of the interference source includes: Time-frequency analysis is performed on the electromagnetic signal data to extract signal features; Based on the extracted signal features, the interference type is obtained; wherein, the interference type includes: natural interference, unintentional interference, and malicious interference; In the case where the interference type is malicious interference, the malicious interference is classified as at least one of continuous wave interference, pulse interference, or frequency sweep interference.

3. The method according to claim 2, characterized in that, The step of performing time-frequency analysis on the electromagnetic signal data and extracting signal features includes: The electromagnetic signal data is subjected to a short-time Fourier transform to obtain the time-frequency distribution of the signal; based on the time-frequency distribution, the spectral features of the signal are extracted. Based on the spectral characteristics of the signal, calculate the frequency gradient of its power and its change within the observation time window; Based on the frequency gradient and changes of the power, the specific type of malicious interference is determined by an interference classification function. And by using the phase difference, signal wavelength, and sensor spacing of the signals received by the sensor array, the direction angle of arrival of the interference source is calculated.

4. The method according to claim 1, characterized in that, The process of integrating the results of the interference feature analysis step with the current flight status information of the UAV to assess and output the electromagnetic threat level includes: The comprehensive threat index is calculated based on the strength and duration of the interference signal and its overlap with the communication and navigation frequency bands of the UAV. By combining information on the UAV's flight altitude, speed, and mission phase, the flight status influencing factors are calculated. And based on the comprehensive threat index and the flight status influence factor, the final electromagnetic threat level is determined.

5. The method according to claim 4, characterized in that, in, The threat levels include at least mild interference, moderate interference, severe interference, and extreme interference. The process involves calling and executing corresponding anti-interference strategies from a strategy library based on the electromagnetic threat level. The intelligent anti-interference decision-making and control subsystem coordinates its response with the flight module, including: For the aforementioned mild interference level, adaptive filtering and enhanced multi-source navigation data fusion are enabled; For moderate interference levels, activate the communication parameter agility mechanism and switch to the backup communication link, while adopting a conservative flight control law; In addition, for severe and extreme interference levels, it triggers an autonomous survival emergency protocol, suspends unnecessary signal transmissions, and executes a preset emergency flight route based on the redundant navigation system.

6. The method according to claim 1, characterized in that, The continuous monitoring of electromagnetic environment changes and the effectiveness of anti-interference strategies, and the dynamic adjustment of anti-interference strategies based on monitoring results, include: If the interference is detected as not being mitigated, the ongoing anti-interference strategy will be automatically upgraded to the strategy corresponding to a higher threat level. If the interference is detected to be weakened or disappear, the anti-interference strategy is gradually downgraded until normal flight mode is restored. Furthermore, it records interference events and system response data, and iteratively optimizes the policy library.

7. An anti-electromagnetic interference system for unmanned aerial vehicles (UAVs), characterized in that, The system includes: an electromagnetic environment sensing subsystem, an intelligent anti-interference decision and control subsystem, and a composite electromagnetic protection subsystem; The electromagnetic environment sensing subsystem is used to collect electromagnetic signal data of the environment; The intelligent anti-interference decision-making and control subsystem is connected to the electromagnetic environment sensing subsystem and is used to perform interference feature analysis, threat assessment, strategy matching and control command generation. The composite electromagnetic protection subsystem is connected to the intelligent anti-interference decision and control subsystem, and is used to receive the control commands and execute corresponding physical layer and signal layer protection actions.

8. The system according to claim 7, characterized in that, The electromagnetic environment sensing subsystem includes: a multi-band field strength monitoring array, an interference feature analysis module, and a source orientation module; The multi-band field strength monitoring array is distributed on the wings and fuselage of the UAV; The interference feature analysis module is used to analyze the monitoring data to identify the type of interference; The source orientation module is used to estimate the direction of the interference source based on the data from the monitoring array.

9. The system according to claim 7, characterized in that, The intelligent anti-interference decision and control subsystem includes: a fusion decision unit and a strategy library executor; The fusion decision unit is used to fuse interference features and flight status information to assess the threat level; The policy library executor is used to invoke pre-stored anti-interference policies and generate control commands based on the threat level.

10. The system according to claim 7, characterized in that, The composite electromagnetic protection subsystem includes: an electromagnetic shielding structure and an adaptive filtering and agile communication module; The electromagnetic shielding structure is used to provide an independent physical shielding space for the core flight control and navigation equipment of the UAV; The adaptive filtering and agile communication module is used to dynamically adjust the filtering parameters and transmission frequency of the communication signal according to the control command.

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