A method and system for active escape of an unmanned aerial vehicle from electromagnetic attack

Abstract

This invention discloses a method and system for active escape from electromagnetic attacks by unmanned aerial vehicles (UAVs), relating to the field of UAV defense technology. It provides an active electromagnetic protection method for UAVs, offering a feasible new approach to electromagnetic pulse protection for low-cost, miniaturized UAVs without adding a large amount of physical shielding and port protection hardware, or significantly increasing the payload of the UAV platform. Overcoming the limitation of existing monitoring equipment being difficult to miniaturize and install on small UAVs, an ultra-low power, miniaturized airborne electromagnetic pulse sensing device is designed. Through a dual-mode dynamic sampling mechanism, it achieves microsecond-level detection of electromagnetic pulse attacks while ensuring the UAV platform's endurance, thus solving the response delay problem.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) defense technology, specifically to a method and system for UAVs to actively escape electromagnetic attacks. Background Technology

[0002] With the increasing prevalence of drone technology in the civilian sector, the importance of counter-drone technology is becoming increasingly prominent. Among various countermeasures, directed energy electromagnetic pulse weapons (such as high-power microwave weapons) pose a significant threat to drones, especially low-cost, miniaturized drone platforms, due to their unique advantages of light-speed attack, wide coverage, and low aiming accuracy requirements. Strong electromagnetic pulses can induce irreversible damage to the electronic systems of drones within microseconds or even nanoseconds, leading to their direct crash.

[0003] Current technological approaches for drones to counter electromagnetic pulse threats have limitations in the following two aspects, making it difficult to implement them in low-cost, miniaturized drone platforms: 1. Hardware Protection: Passive concealment technology faces a trade-off between weight, cost, and protective effectiveness. Existing technologies primarily rely on spatial radiation shielding (such as shielding the airframe, cables, and electronic modules) and conducted filtering protection (such as adding pulse limiters and filters). While these methods can attenuate field strength to some extent, the added bulky shielding and protective hardware significantly increase the weight and size of the UAV, severely sacrificing its aerodynamic performance and endurance. This protection solution, which sacrifices core platform performance, lacks practical engineering value for consumer-grade or small military UAVs that are highly sensitive to weight and cost.

[0004] 2. Behavioral Strategy Level: Existing evasion strategies suffer from intelligent deficiencies of "slow perception and blind decision-making." Firstly, at the perception level, existing airborne electromagnetic environment monitoring technologies primarily serve communication anti-jamming or geofencing. Their sampling rates and algorithm response speeds are insufficient to effectively capture transient electromagnetic pulse attacks with nanosecond-level rising edges, resulting in a failure to provide timely warnings. Secondly, at the decision-making level, the emergency landing or automatic return-to-home strategies adopted by some drones after detecting interference are pre-set, rigid, and fixed procedures, unable to perceive and adapt to dynamically changing electromagnetic threat environments in real time. In malicious scenarios, the return-to-home route or landing area may point directly to the core area of ​​the attack source, essentially "walking into a trap," with an extremely low probability of survival.

[0005] Therefore, existing protection technologies are difficult to apply to commercial, miniaturized drones, and cannot achieve the process from perception to decision-making and then to active avoidance. There is a lack of a low-cost, miniaturized, real-time solution that can guide drones to actively escape areas with strong electromagnetic threats. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for active escape from electromagnetic attacks for unmanned aerial vehicles (UAVs) in response to the aforementioned problems.

[0007] The technical solution of the present invention is as follows: A method for active escape from electromagnetic attacks by unmanned aerial vehicles (UAVs) includes the following steps: Dynamic sensing: Continuously monitors the intensity of the environmental electric field by deploying at least three electric field sensors on the drone; Attack detection: When the electric field strength value detected by any electric field sensor exceeds the preset strength threshold, and the rate of change of the electric field strength exceeds the preset rate of change threshold, the drone is determined to be under electromagnetic pulse attack and high sampling mode is triggered. Gradient analysis: In high sampling mode, the spatial gradient vector of electric field intensity at the location of the UAV is calculated based on the real-time data collected by each electric field sensor. Escape decision: Based on the spatial gradient vector of the electric field intensity, a dynamic weighted escape direction decision algorithm is used to calculate the optimal escape direction vector; Active escape: Generate flight control commands based on the optimal escape direction vector to control the UAV to escape away from the direction where the electric field strength increases the fastest.

[0008] Active protection enhances the survivability of drones: Unlike traditional passive shielding approaches, the proposed "dynamic sampling + gradient calculation" active protection concept transforms drones from passively being attacked to actively escaping, overcoming the limitations of traditional protection technologies such as "returning to base upon failure leading to mission failure, immediate landing potentially resulting in capture, and critical system restarts easily causing total loss of control." By calculating the optimal escape path in real time, drones can escape the core threat zone with maximum efficiency while maintaining the ability to continue mission execution, significantly improving mission success rate and equipment survivability.

[0009] Furthermore, the gradient analysis includes: Drone at the time point Deployed in different locations on the fuselage An electric field sensor measures a scalar value of the electric field intensity. ; The electric field intensity at the current moment is fitted using the spatial difference method. Three-dimensional spatial gradient in the UAV body coordinate system The calculation formula is as follows: , in, It is the Jacobian matrix, which is a function of the electric field sensor coordinates. It is the first Each electric field sensor at time point The difference between the time and the average of all electric field sensor readings is expressed as follows: , , in, No. Each electric field sensor at time point The reading at that time, For all sensors at time points The average reading at that time; Therefore, this gradient vector The direction of the electric field strength at the current position is the direction in which the increase is the fastest, and its magnitude represents the rate of increase.

[0010] Furthermore, the dynamically weighted escape direction decision algorithm is as follows: Based on multiple historical gradient vectors within the current time window and the previous time window, a weighted sum is performed, and the sum is inverted and normalized to obtain the optimal escape direction vector.

[0011] Furthermore, at a certain point in time The final escape direction unit vector The calculation method is as follows: , in, It is the size of the time window. This is the sampling time interval in high sampling rate mode. Is assigned to the first Weighting factors for historical gradient data.

[0012] Furthermore, the weighting factor The calculation method is as follows: , in, It is the confidence level of the gradient strength. It is the time-based confidence level, and its sum is 1, that is... It is used for parameter balancing and adjustment.

[0013] Furthermore, the electric field sensor operates in a low sampling rate monitoring mode by default, with a sampling rate below 10Hz; Upon determining that an electromagnetic pulse attack has occurred, all electric field sensors are switched to high sampling rate mode, with a sampling rate of 1 Hz or higher.

[0014] Low power consumption design: The proposed electromagnetic environment dynamic sampling mechanism keeps the system in an ultra-low power low sampling monitoring state under normal conditions, and only enters a high sampling rate state after sensing an electromagnetic attack. The overall solution has little impact on the drone's endurance.

[0015] Furthermore, the attack determination specifically includes: When any sensor first detects that the electric field strength exceeds a preset strength threshold, rapid continuous detection is initiated. If multiple tests are performed within a preset short time interval, and the rate of change of electric field intensity in each test exceeds a preset rate of change threshold, then it is ultimately determined that the device has been subjected to an electromagnetic pulse attack.

[0016] Improve reliability and reduce false alarm rate in complex environments: Through the dual criterion triggering mechanism of "field strength amplitude - field strength change rate", it can effectively distinguish between strong electromagnetic pulse attacks and ordinary wireless interference or electromagnetic environmental noise, reduce false alarm rate, improve the accuracy of threat alarm, and ensure the correctness of escape strategy.

[0017] This application also includes a UAV anti-electromagnetic attack active escape system, characterized by the application of a UAV anti-electromagnetic attack active escape method, including: The electric field sensor array consists of at least three electric field sensors distributed on the body of the UAV. The dynamic sampling control circuit, connected to the electric field sensor array, is configured to manage the switching of the electric field sensor's operating mode. The gradient analysis calculation unit, connected to the dynamic sampling control circuit, is configured to calculate the spatial gradient vector of the electric field intensity after the high sampling mode is triggered. An escape instruction generator, connected to a gradient analysis computation unit, is configured to run a dynamically weighted escape direction decision algorithm to generate the optimal escape direction vector; The escape command generator outputs commands that can be sent to the drone's flight control system to drive the drone to perform an escape maneuver.

[0018] Furthermore, the electric field sensor is a MEMS electric field sensor or an isotropic electric field probe, and is deployed at the end of the UAV's arm, wing, or under the fuselage, with the distance between any two sensors not less than 15 centimeters.

[0019] Low-cost design improves the feasibility of drone protection: By using low-cost commercial MEMS sensors and intelligent algorithms to replace shielding materials that severely affect flight endurance, the system only senses the amplitude of the electric field strength in the environment and does not monitor time-domain or frequency-domain data. This reduces the cost of monitoring hardware and computational workload, enabling the miniaturization and low cost of drones to resist strong electromagnetic pulses. The cost can be controlled to less than 10% of traditional protection solutions, making it possible to apply strong electromagnetic protection on a large scale in consumer drones and low-cost military drones.

[0020] This application also includes a drone equipped with a drone anti-electromagnetic attack active escape system.

[0021] Compared with existing technologies, the advantages of this invention are: 1. It provides an active electromagnetic protection method for drones, offering a feasible new approach to electromagnetic pulse protection for low-cost, miniaturized drones without adding a large amount of physical shielding and port protection hardware or significantly increasing the load on the drone platform. 2. Overcoming the limitation that existing monitoring equipment is difficult to miniaturize and cannot be mounted on small drones, an ultra-low power consumption and miniaturized airborne electromagnetic pulse sensing device was designed. Through a dual-mode dynamic sampling mechanism, it can achieve microsecond-level detection of electromagnetic pulse attacks while ensuring the drone platform's endurance, thus solving the response delay problem. 3. Overcoming the blindness of traditional UAV evasion strategies, it provides an intelligent escape decision algorithm based on real-time electric field intensity spatial gradient analysis, enabling UAVs to autonomously determine the safe direction with the fastest field strength decay and escape, rather than simply landing or returning, thereby significantly improving the survival probability in complex electromagnetic threat environments. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the method described in this application.

[0023] Figure 2 This is a flowchart illustrating the dynamic sampling triggering process for an electromagnetic pulse attack environment as described in this application.

[0024] Figure 3 This is a schematic diagram illustrating the definition of the UAV coordinate system in a specific application example of this application.

[0025] Figure 4 This is a schematic diagram of the arrangement of the UAV electric field sensor in a specific application example of this application.

[0026] Figure 5 This is a schematic diagram illustrating an electromagnetic pulse attack source located on the ground to the right front of the drone in a specific use case of this application. Detailed Implementation

[0027] It should be noted that 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 one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0028] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0029] Please see Figure 1-2 A novel active escape method for unmanned aerial vehicles (UAVs) against electromagnetic attacks is proposed. This method constructs an active protection mechanism based on "dynamic perception—escape decision generation—autonomous execution," and its main principle diagram is shown below. Figure 1 As shown. Includes the following steps: Dynamic sensing: Continuously monitors the intensity of the environmental electric field by deploying at least three electric field sensors on the drone; Attack detection: When the electric field strength value detected by any electric field sensor exceeds the preset strength threshold, and the rate of change of the electric field strength exceeds the preset rate of change threshold, the drone is determined to be under electromagnetic pulse attack and high sampling mode is triggered. Gradient analysis: In high sampling mode, the spatial gradient vector of electric field intensity at the location of the UAV is calculated based on the real-time data collected by each electric field sensor. Escape decision: Based on the spatial gradient vector of the electric field intensity, a dynamic weighted escape direction decision algorithm is used to calculate the optimal escape direction vector; Active escape: Generate flight control commands based on the optimal escape direction vector to control the UAV to escape away from the direction where the electric field strength increases the fastest.

[0030] The dynamic sensing and attack determination process employs a dynamic sampling triggering mechanism for the electromagnetic pulse attack environment. The sampling rate of the electric field sensor is dynamically adjusted based on the real-time value of the monitored electric field strength. The electric field sensor operates by default in a low sampling rate monitoring mode, with a sampling rate range below 10Hz, typically 0.1Hz to 1Hz, in a low-power mode. When any electric field sensor detects a field strength value exceeding a set threshold (e.g., 200V / m), rapid continuous detection is immediately initiated, performing three detections with 1ms intervals. When the rate of change of field strength exceeds a set slope (e.g., 50V / m / ms), the system determines that the UAV is under electromagnetic pulse attack and immediately switches all electric field sensors to a high sampling rate mode, with a sampling rate range above 1Hz, typically 10Hz to 10kHz, entering high-speed monitoring to acquire the electromagnetic pulse attack intensity at the UAV's location in real time, providing data for subsequent gradient vector calculation and escape decision calculation. The dynamic sampling trigger flowchart is shown below. Figure 2 As shown.

[0031] To measure the spatial rate of change (i.e., gradient) of the electric field intensity in real time, spatiotemporal data from multiple distributed sensors are used for calculation, clarifying how to obtain a continuous gradient field estimate from discrete spatial point measurements. The gradient analysis includes an escape algorithm based on the spatiotemporal field intensity gradient vector. Drone at the time point Deployed in different locations on the fuselage An electric field sensor measures a scalar value of the electric field intensity. ; The electric field intensity at the current moment is fitted using the spatial difference method. Three-dimensional spatial gradient in the UAV body coordinate system The calculation formula is as follows: , in, It is the Jacobian matrix, which is a function of the electric field sensor coordinates. It is the first Each electric field sensor at time point The difference between the time and the average of all electric field sensor readings is expressed as follows: , , in, No. Each electric field sensor at time point The reading at that time, For all sensors at time points The average reading at that time; Therefore, this gradient vector The direction of the electric field strength at the current position is the direction in which the increase is the fastest, and its magnitude represents the rate of increase.

[0032] To improve the robustness of escape decision-making and avoid abnormal changes in decision-making caused by instantaneous jumps in data from a single electric field sensor, a dynamic weighting mechanism based on confidence level is introduced.

[0033] This algorithm is not based on instantaneous data acquisition, nor does it simply use the direction of the negative gradient at the current moment. Instead, it is based on historical gradient data within a time window, and calculates a stable and reliable escape direction through weighted fusion, considering both gradient magnitude and time factors. The calculation method incorporates noise filtering, making the decision smoother and more reliable. The dynamic weighted escape direction decision algorithm is as follows: Based on multiple historical gradient vectors within the current time window and the previous time window, a weighted sum is performed, and the sum is inverted and normalized to obtain the optimal escape direction vector.

[0034] At the point of time The final escape direction unit vector The calculation method is as follows: , in, It is the size of the time window. This is the sampling time interval in high sampling rate mode. Is assigned to the first Weighting factors for historical gradient data.

[0035] Weighting factors The calculation method is as follows: , in, It is the confidence level of the gradient strength. It is the time-based confidence level, and its sum is 1, that is... It is used for parameter balancing and adjustment.

[0036] Low-cost design improves the feasibility of drone protection: By using low-cost commercial MEMS sensors and intelligent algorithms to replace shielding materials that severely affect flight endurance, the system only senses the amplitude of the electric field strength in the environment and does not monitor time-domain or frequency-domain data. This reduces the cost of monitoring hardware and computational workload, enabling the miniaturization and low cost of drones to resist strong electromagnetic pulses. The cost can be controlled to less than 10% of traditional protection solutions, making it possible to apply strong electromagnetic protection on a large scale in consumer drones and low-cost military drones.

[0037] Low power consumption design: The proposed electromagnetic environment dynamic sampling mechanism keeps the system in an ultra-low power low sampling monitoring state under normal conditions, and only enters a high sampling rate state after sensing an electromagnetic attack. The overall solution has little impact on the drone's endurance.

[0038] Improve reliability and reduce false alarm rate in complex environments: Through the dual criterion triggering mechanism of "field strength amplitude - field strength change rate", it can effectively distinguish between strong electromagnetic pulse attacks and ordinary radio interference or electromagnetic environmental noise, reduce false alarm rate, improve the accuracy of threat alarms, and ensure the correctness of escape strategy.

[0039] This application also includes an active escape system for unmanned aerial vehicles (UAVs) against electromagnetic attacks, employing a method for active escape from electromagnetic attacks against UAVs, comprising: Electric field sensor array: composed of indivual( The electric field sensors are distributed with a spacing of ≥15cm. They are usually installed at the end of the arm of a rotary-wing UAV or under the wing and fuselage of a fixed-wing UAV. They can be MEMS sensors or isotropic probes.

[0040] Dynamic sampling control circuit: The electric field sensors operate by default in a low sampling rate monitoring mode, typically with a sampling rate of 0.2Hz to 1Hz, continuously monitoring the background field strength under low power consumption. When any electric field sensor detects a field strength value exceeding a set threshold (e.g., 200V / m) and the rate of change of the field strength exceeds a set slope (e.g., 50V / m / ms), the system immediately determines that it is under an electromagnetic pulse attack and immediately switches all electric field sensors to a high sampling rate mode of 10Hz to 10kHz to obtain the electromagnetic pulse attack intensity at the location of the UAV in real time.

[0041] Gradient Analysis Calculation Unit: When the high sampling mode is activated, the gradient analysis calculation unit reads the data from each electric field sensor in real time and calculates the gradient in real time according to the escape algorithm based on the spatiotemporal field strength gradient vector. The electric field distribution in physical space is transformed into a computable mathematical vector. The three-dimensional spatial gradient vector of the electric field intensity is estimated by calculating the spatial difference. The direction of this vector points to the direction of the fastest increase in field intensity, and the optimal escape decision is made accordingly.

[0042] Escape instruction generator: Based on the calculation results of the gradient analysis unit, it substitutes the current and historical gradients into the dynamic weighted escape direction decision algorithm to calculate the current optimal escape direction vector. The system simultaneously considers gradient magnitude and time factors to calculate a stable and reliable escape direction, which is highly likely to deviate from the attack source or penetrate areas with weak field strength. After generating control commands, the drone is driven to escape with maximum power.

[0043] Flight control system: Executes flight commands issued by the escape command generator, and sets the direction vector... The command is broken down into yaw angle adjustment commands and pitch / roll commands, which control the UAV to escape the electromagnetic pulse attack coverage area with maximum power or a preset escape speed. The system continuously monitors the field strength at a high sampling rate until the electric field strength sensed by the electric field sensor array is lower than the set safety threshold. Then, it automatically returns to the low sampling rate monitoring mode to prepare for the next threat.

[0044] The system includes a dynamic sampling and triggering mechanism for electromagnetic pulse attack environments: The sampling rate of the electric field sensor is dynamically adjusted based on the real-time value of the monitored electric field strength. The electric field sensor operates by default in a low sampling rate monitoring mode, with a sampling rate range of 0.2Hz to 1Hz, operating in a low-power condition. When any electric field sensor detects a field strength value exceeding a set threshold (e.g., 200V / m), rapid continuous detection is immediately initiated, performing three detections with 1ms intervals. When the rate of change of field strength exceeds a set slope (e.g., 50V / m / ms), the system determines that the UAV is under electromagnetic pulse attack and immediately switches all electric field sensors to a high sampling rate mode, with a sampling rate range of 10Hz to 10kHz, entering high-speed monitoring to acquire the electromagnetic pulse attack intensity at the UAV's location in real time, providing data for subsequent gradient vector calculation and escape decision calculation. The dynamic sampling trigger flowchart is shown below. Figure 2 As shown.

[0045] This application also includes a drone equipped with a drone anti-electromagnetic attack active escape system.

[0046] In another specific embodiment, a specific example of using the active escape method for anti-electromagnetic attacks of a drone according to this application is disclosed: Design input: The drone is a quadcopter with a diagonal motor spacing of 50 cm. The coordinate system is defined as follows: right-hand direction as the X-axis, forward direction as the Y-axis, and upward direction as the Z-axis. Figure 3 As shown.

[0047] The drone is equipped with three electric field sensors, arranged non-coplanarly, with the specific locations as follows: Figure 4 As shown.

[0048] The electromagnetic pulse attack source was located on the ground to the right front of the drone, such as... Figure 5 As shown, when an electromagnetic pulse propagates in space, its field strength decreases approximately with the square of the distance.

[0049] Parameter settings: In low-power mode, the sensor sampling frequency is set to 1Hz; in high-sampling mode, the sampling rate increases to 10kHz. The threshold for the field strength under attack threat is preset to 200V / m, and the threshold for the rate of change of the field strength under attack threat is preset to 50000V / m / s. (Time window...) Set to 2 (i.e., considering the current and two past time steps, for a total of 3 data points), weight parameters gradient strength confidence. Set to 0.7, time-based confidence level Set it to 0.3; Calculation process: The system operates in low-power mode, with the electric field sensor array sampling at a frequency of 1Hz, once every 1 second, and the background field strength of the space where the UAV is located is kept below 10V / m.

[0050] An electromagnetic pulse attack occurs, and at time t=0, the leading edge of the electromagnetic pulse reaches the drone.

[0051] At t=1ms, the reading of S1 increases to 1050V / m, S2 to 600V / m, and S3 to 500V / m.

[0052] If the system detects that the maximum value of any sensor in the array is greater than 200V / m (the preset threshold for the field strength of the attack threat), and that there is a rate of change of (1050-10) / 0.001 = 1040000V / m / s in the array that is greater than 50000V / m / s (the preset threshold for the rate of change of the field strength of the attack threat), it immediately triggers a high-sampling mode, increasing the sampling rate to 10kHz, with intervals... =0.1ms.

[0053] Gradient calculation begins; the spatial gradient of the electric field at time t=1ms is calculated. .

[0054] First, construct the constant Jacobian matrix. Since the electric field sensor's position is fixed, it is a constant matrix. The design matrix is ​​constructed using the sensor coordinates:

[0055] Calculate the electric field difference vector The average field strength reading at the current time (t) =(1050+600+500) / 3≈716.7V / m.

[0056] Calculate the difference vector:

[0057] Substitute into the formula to calculate the gradient :

[0058] in, for:

[0059] Its inverse matrix for:

[0060]

[0061] Substituting into the formula, the gradient is finally obtained. :

[0062] Preliminary conclusions are drawn that at t=1ms, the electric field gradient is (900, 433.2, 2167) V / m. This indicates that the electric field increases most rapidly in the +X (right), +Y (front), and +Z (up) directions, with the strongest upward increasing trend.

[0063] Start calculating the escape direction decision, assuming it's at time t=2ms, and calculate the current gradient. Substitute the historical data at t-0.1ms and t-0.2ms into the dynamic weighted decision formula.

[0064] At t=2ms, the readings of S1 are 1800V / m, S2 is 800V / m, and S3 is 950V / m. The average electric field strength is Ê(t)≈1183.3V / m. , module length ≈3085.

[0065] Calculate the readings at t=1.9ms: S1 = 1750V / m, S2 = 430V / m, and S3 = 2150V / m. , Module length ≈2790.

[0066] At t=1.8ms, the readings for S1 are 1500V / m, S2 is 400V / m, and S3 is 2000V / m. , Module length ≈2540.

[0067] Start calculating weights First obtain =max(2540,2790,3085) =3085. Therefore, when k=0 (current t=1.9ms), =0.7*(3085 / 3085)+0.3*(3-0)=1.6. When k=1 (previous step t=1.9ms), =0.7*(2790 / 3085)+0.3*(3-1)≈1.233. When k=2 (t=1.8ms in the first two steps), =0.7*(2540 / 3085)+0.3*(3-2)≈0.876.

[0068] Then calculate the weighted average gradient: = 1.6*(2000,467.2,2333)+1.233*(1750,430,2150)+0.876*(1500,400,2000)= =(6671.8,1628.1,8136).

[0069] Normalize the weighted result to calculate the escape direction vector: The calculated modulus length is approximately 10650. therefore, .

[0070] Application of results: The final escape direction unit vector output to the UAV flight control system is: (-0.626, -0.153, -0.764).

[0071] The generated specific flight control commands are: descend sharply to the lower left, continue maximum power escape, maintain a high sampling rate to monitor the electric field strength until the sensor array is all <200V / m.

[0072] During the escape process, the escape direction vector is recalculated based on the real-time measured field strength data, and the time interval of the escape direction is adjusted again, which can be set according to the actual use.

[0073] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. A method for active escape from electromagnetic attacks by unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: Dynamic sensing: Continuously monitors the intensity of the environmental electric field by deploying at least three electric field sensors on the drone; Attack detection: When the electric field strength value detected by any electric field sensor exceeds the preset strength threshold, and the rate of change of the electric field strength exceeds the preset rate of change threshold, the drone is determined to be under electromagnetic pulse attack and high sampling mode is triggered. Gradient analysis: In high sampling mode, the spatial gradient vector of electric field intensity at the location of the UAV is calculated based on the real-time data collected by each electric field sensor. Escape decision: Based on the spatial gradient vector of the electric field intensity, a dynamic weighted escape direction decision algorithm is used to calculate the optimal escape direction vector; Active escape: Generate flight control commands based on the optimal escape direction vector to control the UAV to escape away from the direction of the fastest increase in electric field strength; The dynamic weighted escape direction decision algorithm is as follows: Based on multiple historical gradient vectors within the current time window and the previous time window, a weighted sum is performed, and the sum is inverted and normalized to obtain the optimal escape direction vector. At the point of time The final escape direction unit vector The calculation method is as follows: ， in, It is the size of the time window. This is the sampling time interval in high sampling rate mode. Is assigned to the first Weighting factors for historical gradient data; The gradient vector; Weighting factors The calculation method is as follows: ， in, It is the confidence level of the gradient strength. It is the time-based confidence level, and their sum is 1, that is... It is used for parameter balancing and adjustment.

2. The method for active escape from electromagnetic attacks by unmanned aerial vehicles according to claim 1, characterized in that, The gradient analysis includes: Drone at the time point Deployed in different locations on the fuselage An electric field sensor measures a scalar value of the electric field intensity. ; The electric field intensity at the current moment is fitted using the spatial difference method. Three-dimensional spatial gradient in the UAV body coordinate system The calculation formula is as follows: ， in, It is the Jacobian matrix, which is a function of the electric field sensor coordinates. It is the first Each electric field sensor at time point The difference between the time and the average of all electric field sensor readings is expressed as follows: ， ， in, No. Each electric field sensor at time point The reading at that time, For all sensors at time points The average reading over time; Therefore, this gradient vector The direction of the electric field strength at the current position is the direction in which the increase is the fastest, and its magnitude represents the rate of increase.

3. The method for active escape from electromagnetic attacks by unmanned aerial vehicles according to claim 1, characterized in that, The electric field sensor operates in a low sampling rate monitoring mode by default, with a sampling rate below 10Hz. Upon determining that an electromagnetic pulse attack has occurred, all electric field sensors are switched to high sampling rate mode, with a sampling rate of 1 Hz or higher.

4. The method for active escape from electromagnetic attacks by unmanned aerial vehicles according to claim 3, characterized in that, The attack determination specifically includes: When any sensor first detects that the electric field strength exceeds a preset strength threshold, rapid continuous detection is initiated. If multiple tests are performed within a preset short time interval, and the rate of change of electric field intensity in each test exceeds a preset rate of change threshold, then it is ultimately determined that the device has been subjected to an electromagnetic pulse attack.

5. A drone anti-electromagnetic attack active escape system, characterized in that, The method for active escape from electromagnetic attacks by a drone as described in any one of claims 1-4 includes: The electric field sensor array consists of at least three electric field sensors distributed on the body of the UAV. The dynamic sampling control circuit, connected to the electric field sensor array, is configured to manage the switching of the electric field sensor's operating mode. The gradient analysis calculation unit, connected to the dynamic sampling control circuit, is configured to calculate the spatial gradient vector of the electric field intensity after the high sampling mode is triggered. An escape instruction generator, connected to a gradient analysis computation unit, is configured to run a dynamically weighted escape direction decision algorithm to generate the optimal escape direction vector; The escape command generator outputs commands that can be sent to the drone's flight control system to drive the drone to perform an escape maneuver.

6. The active escape system for unmanned aerial vehicles against electromagnetic attacks according to claim 5, characterized in that, The electric field sensor is a MEMS electric field sensor or an isotropic electric field probe, and is deployed at the end of the UAV arm, wing, or under the fuselage, with a distance of not less than 15 centimeters between any two sensors.

7. A drone, characterized in that, It is equipped with an active escape system against electromagnetic attacks for unmanned aerial vehicles as described in claim 5 or 6.

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