Unmanned aerial vehicle navigation decoy method based on multi-navigation signal interference

Abstract

The invention provides an unmanned aerial vehicle navigation decoy method based on multi-navigation signal interference, and relates to the field of unmanned aerial vehicle management and control algorithms, and the method comprises the following steps: Sp1, environment initialization; sp2, generating an interference signal; sp3, injecting a decoy signal; sp4, dynamic signal regulation and control; sp5, intelligent feedback optimization is carried out; sp6, target decoy realization: multi-sensor perception and a multi-band programmable radio frequency front end are utilized, directional interference and high-precision decoy can be realized for different types of unmanned aerial vehicles with different navigation protocols, irrelevant aviation communication or other equipment is prevented from being interfered, virtual navigation tracks are injected on the flight paths of the unmanned aerial vehicles, and the unmanned aerial vehicles with different navigation protocols can be decorated. The flight direction of the controlled unmanned aerial vehicle is gradually changed or the controlled unmanned aerial vehicle safely lands, secondary risks such as falling objects and people injury caused by fragments possibly caused by direct'hard destroy 'are greatly reduced, forged signals can be gradually weakened after a decoy or protection task is completed, the controlled unmanned aerial vehicle is smoothly switched back to real navigation, and the influence on the environment and other aircrafts is reduced.

Description

[0001] Algorithm field

[0002] The present invention relates to the field of unmanned aerial vehicle (UAV) control algorithms, and in particular to a UAV navigation deception method based on multi-navigation signal interference.

[0003] Background Algorithm

[0004] According to the navigation signal interference warning system, method and storage medium of the unmanned aerial vehicle disclosed in the Chinese patent publication number "CN118604849A", the system includes: a signal detection module, which is used to detect the first navigation signal of the unmanned aerial vehicle in real time, wherein the first navigation signal includes a global satellite navigation signal provided by multiple satellite systems; a signal evaluation module, which is used to perform signal quality evaluation on the first navigation signal to obtain a signal evaluation index, wherein the signal evaluation index is used to characterize the degree of interference of the unmanned aerial vehicle; a data analysis module, which is used to perform interference intensity analysis on the first navigation signal when the signal evaluation index meets the target condition to obtain an interference intensity index, wherein the target condition is used to characterize the presence of an interference abnormal signal in the first navigation signal; and a warning decision module, which is used to generate an interference warning strategy according to the interference intensity index.

[0005] Existing technologies rely too much on capture-type or forced-landing interference, resulting in a certain risk of damage to personnel and finances in the coverage area caused by captured drones during forced landing or interference. Summary of the invention

[0006] Technical issues solved

[0007] In view of the shortcomings of the existing algorithms, the present invention provides a UAV navigation spoofing method based on multi-navigation signal interference to solve the problems of the prior art.

[0008] Technical Solution

[0009] To achieve the above objectives, the present invention is implemented by the following algorithm scheme: a UAV navigation deception method based on multi-navigation signal interference, the method comprising the following steps:

[0010] Sp1, environmental initialization: including an environmental perception module, which scans the target area, obtains the signal characteristic data of the UAV, including the navigation protocol type, receiving frequency band, signal strength and dynamic flight parameters, and builds a navigation system model of the UAV;

[0011] Sp2, interference signal generation: including a multi-band interference module, the multi-band interference module generates a pseudo-random noise interference signal, a frequency drift interference signal, a phase interference signal and a pseudo signal injection signal, and uses a simulated satellite signal source to generate a fake navigation signal;

[0012] Sp3, decoy signal injection: including a multi-protocol fusion module, which constructs a multi-protocol combination signal that matches the navigation protocol used by the target UAV, and injects a virtual satellite signal by adjusting the signal power and transmission angle;

[0013] Sp4, dynamic signal control: including a dynamic signal control module and a signal synchronization module, the dynamic signal control module adjusts the intensity, frequency and phase parameters of the pseudo signal in real time to simulate the dynamic Doppler effect;

[0014] Sp5, Intelligent Feedback Optimization: Use artificial intelligence algorithms to analyze the response of the drone navigation system in real time and dynamically optimize the decoy signal parameters;

[0015] Sp6. Target deception is achieved: The UAV is guided to deviate from the original flight mission trajectory through a virtual trajectory, and the pseudo signal strength is gradually reduced after the UAV enters the predetermined area to restore the real navigation signal.

[0016] Preferably, the multi-band interference module includes multiple programmable RF front-ends for achieving synchronous interference of L1, L2 and L5 frequency bands, using a high-precision frequency synthesis algorithm to generate a frequency-stable interference signal, and optimizing the signal strength through an adaptive power regulation circuit to match the receiving characteristics of the target drone.

[0017] Preferably, the multi-protocol fusion module adopts a multi-protocol parsing and reconstruction algorithm to generate a fake signal by real-time decoding and simulation of the navigation protocol of the target UAV. The fake signal includes a timestamp, ephemeris data, and multi-path delay characteristics to ensure the authenticity and timeliness of the decoy signal.

[0018] Preferably, the dynamic signal control module includes a Doppler effect simulation unit and a real-time path prediction unit, which calculate the frequency drift of the pseudo signal based on the flight speed and heading of the target UAV, and adjust the phase error to enhance the concealment of the signal interference.

[0019] Preferably, the intelligent feedback optimization step adjusts the injection position, intensity and trajectory deviation parameters of the decoy signal based on the reinforcement learning algorithm and environmental perception data, and predicts its path change trend by establishing a mathematical model of the drone response, thereby optimizing the decoy strategy.

[0020] Preferably, the environmental perception module achieves high-precision positioning and navigation status monitoring through a multi-sensor data fusion algorithm. The environmental perception module includes a radar sensor, an infrared sensor and a radio signal receiving device. The radar sensor is used to obtain the three-dimensional spatial position of the drone, the infrared sensor is used to detect the speed and direction of the drone, and the radio signal receiving device is used to capture the characteristic parameters of the navigation signal.

[0021] Preferably, the decoy target implementation includes constructing a dynamically adjusted flight trajectory through a virtual trajectory generation unit, wherein the flight trajectory is generated based on the inference of the target UAV mission parameters, and adapts to the changes in the flight state of the UAV through real-time updates, and triggers a signal recovery mechanism when the UAV enters a predetermined area.

[0022] Preferably, the signal synchronization module includes a high-precision time synchronization unit and a pseudo-signal calibration unit, and the signal synchronization module is synchronized with the time of the real navigation signal, and combined with the error correction algorithm to achieve the fusion of the decoy signal and the real signal, thereby avoiding abnormal detection.

[0023] Preferably, the drone navigation spoofing method is used for intercepting illegal drones in airport areas, protecting the security of military bases, and testing the anti-interference performance of navigation systems.

[0024] Beneficial Effects

[0025] The present invention provides a UAV navigation deception method based on multi-navigation signal interference, which has the following beneficial effects:

[0026] 1. The present invention utilizes multi-sensor perception and multi-band programmable RF front-end to achieve directional interference and high-precision deception for drones of different types and navigation protocols, avoiding interference with irrelevant aviation communications or other equipment. By injecting virtual navigation tracks into the drone's track, it can gradually change its flight direction or land safely, greatly reducing the secondary risks of falling objects, debris and other injuries caused by direct "hard destruction". After completing the deception or protection mission, the forged signal can be gradually weakened, allowing the controlled drone to smoothly switch back to real navigation, reducing the impact on the environment and other aircraft.

[0027] 2. The present invention adopts multi-band and multi-protocol fusion, can simultaneously process multi-mode satellite navigation signals such as GPS, GLONASS, Beidou, and generate fake signals in real time for the protocol used by the target UAV, ensuring feasibility in various complex electromagnetic environments. By real-time monitoring of the speed, position and heading of the target UAV, the frequency offset and phase of the interference signal are dynamically adjusted, and the Doppler effect during relative motion of the UAV is simulated to enhance the authenticity and concealment of the decoy signal. The reinforcement learning algorithm can adapt to the anti-interference mechanism of different types of UAVs in continuous interaction and trial, iteratively optimize the interference parameters and decoy strategies, and enhance the adaptability of the entire process. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a work flow chart of the present invention;

[0029] Figure 2 It is a system module composition diagram of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the algorithm scheme in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary algorithm personnel in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0032] like Figure 1-2 As shown, a method for UAV navigation deception based on multi-navigation signal interference includes the following steps:

[0033] Sp1. Environment initialization: including environment perception module, which scans the target area, obtains the signal characteristic data of the UAV, including the navigation protocol type, receiving frequency band, signal strength and dynamic flight parameters, and builds the navigation system model of the UAV. The environment perception module realizes high-precision positioning and navigation status monitoring through multi-sensor data fusion algorithm. The environment perception module includes radar sensor, infrared sensor and radio signal receiving device. The radar sensor is used to obtain the three-dimensional spatial position of the UAV, the infrared sensor is used to detect the speed and direction of the UAV, and the radio signal receiving device is used to capture the characteristic parameters of the navigation signal;

[0034] Sp2, interference signal generation: including a multi-band interference module, which generates pseudo-random noise interference signals, frequency drift interference signals, phase interference signals and pseudo signal injection signals, and uses simulated satellite signal sources to generate fake navigation signals; the multi-band interference module includes multiple programmable RF front ends, which are used to achieve synchronous interference in the L1, L2 and L5 frequency bands, and use high-precision frequency synthesis algorithms to generate frequency-stable interference signals, and optimize signal strength through adaptive power regulation circuits to match the receiving characteristics of the target drone;

[0035] Sp3, decoy signal injection: including a multi-protocol fusion module, which constructs a multi-protocol combination signal that matches the navigation protocol used by the target UAV, and injects a virtual satellite signal by adjusting the signal power and transmission angle. The multi-protocol fusion module uses a multi-protocol parsing and reconstruction algorithm to generate a forged signal by real-time decoding and simulation of the navigation protocol of the target UAV. The forged signal includes a timestamp, ephemeris data, and multi-path delay characteristics to ensure the authenticity and timeliness of the decoy signal;

[0036] Sp4, dynamic signal control: including dynamic signal control module and signal synchronization module, the dynamic signal control module adjusts the strength, frequency and phase parameters of the pseudo signal in real time to simulate the dynamic Doppler effect, the dynamic signal control module includes a Doppler effect simulation unit and a real-time path prediction unit, the Doppler effect simulation unit and the real-time path prediction unit calculate the frequency drift of the pseudo signal based on the flight speed and heading of the target UAV, and adjust the phase error to enhance the concealment of the signal interference;

[0037] Sp5, Intelligent Feedback Optimization: Use artificial intelligence algorithms to analyze the response of the drone navigation system in real time and dynamically optimize the decoy signal parameters. The intelligent feedback optimization step adjusts the injection position, intensity and trajectory offset parameters of the decoy signal based on the reinforcement learning algorithm and environmental perception data. By establishing a mathematical model of the drone's response, the path change trend is predicted, thereby optimizing the decoy strategy;

[0038] Sp6. Target deception: Guide the UAV to deviate from the original flight mission trajectory through a virtual trajectory, and gradually reduce the strength of the false signal after the UAV enters the predetermined area to restore the real navigation signal. The target deception includes constructing a dynamically adjusted flight trajectory through a virtual trajectory generation unit. The flight trajectory is generated based on the inference of the target UAV mission parameters, and is updated in real time to adapt to the changes in the flight status of the UAV, triggering the signal recovery mechanism when the UAV enters the predetermined area.

[0039] The signal synchronization module includes a high-precision time synchronization unit and a pseudo-signal calibration unit. The signal synchronization module is synchronized with the time of the real navigation signal and combined with the error correction algorithm to achieve the fusion of the decoy signal and the real signal, thereby avoiding abnormal detection.

[0040] In the environment initialization and multi-sensor data fusion of step Sp1, we usually use the target UAV positioning and navigation system model based on the state space, let x k represents the state vector of the drone at time k (including position, speed, heading, etc.), z k Represents multi-sensor observations (including radar distance and azimuth information, infrared sensor speed and direction measurement, radio signal strength and frequency information, etc.). For the process model of the system, it is expressed as:

[0041] x k+1 =F k x k +w k ,

[0042] where F k is the state transfer matrix (determined by the kinematic and dynamic models of the drone), w k is the process noise (assumed to be white noise, The observation model is expressed as:

[0043] z k =H k x k +v k ,

[0044] Among them, H k is the observation matrix, which is used to map the state vector to the measurement domain of each sensor, v k is the observation noise (also assumed to be white noise, ). In practical applications, the extended Kalman filter (EKF) or unscented Kalman filter (UKF) is used to realize multi-sensor data fusion. By continuously iteratively minimizing the error between prediction and observation, the high-precision position, speed and heading estimation of the UAV is obtained, providing real-time target situation information for subsequent deception and interference. Secondly, in steps Sp2 and Sp3, it is necessary to establish a hybrid model of the navigation signal and the interference signal. Assume that the real navigation signal in the frequency domain is s r (t), the interference signal generated by the multi-band interference module is s j (t), where s j (t) is further subdivided into pseudo-random noise interference, frequency drift interference, phase interference and pseudo signal injection, etc., and the combined received signal is expressed as:

[0045] s rx (t) = α r s r (t)+α j s j (t)+n(t),

[0046] where α r ,α j are the amplitude weights of the real signal and the interference signal respectively, and n(t) is Gaussian white noise. When it is necessary to generate a fake satellite signal, assuming that the target drone uses a certain navigation protocol (GPSL1 signal), the signal has a spread spectrum code and navigation data. The fake signal generated by the multi-protocol fusion module is expressed as:

[0047]

[0048] Where C(·) is the spread spectrum code signal, D(·) is the navigation data bit stream, τ is the time delay (used to simulate the propagation delay), f cis the center frequency (L1=1575.42MHz), Δf and Δφ represent the simulated frequency offset and phase error respectively, which are used to cooperate with the local receiver of the target drone to deceive. In order to achieve precise alignment with the real navigation signal in time, the signal synchronization module uses a high-precision time synchronization unit to ensure the accuracy of τ, and combines the error correction algorithm in the pseudo-signal calibration unit, so that the time, phase and other characteristics of the fake signal observed at the target drone end can be seamlessly integrated with the real navigation signal to avoid being identified by the anomaly detection mechanism. Furthermore, in step Sp4, the dynamic signal control module needs to adjust the frequency, phase and signal amplitude in real time to simulate the Doppler effect of the drone relative to the satellite or interference source, which is specifically expressed as:

[0049]

[0050] Where v is the relative speed of the target drone relative to the interference source or satellite signal, and θ is the angle between the velocity vector and the signal propagation direction. c is the speed of light. On this basis, the dynamic signal control module also iteratively calculates the correction values ​​of Δf and Δφ according to the flight status information (position, speed, acceleration, heading, etc.) given by the real-time path prediction unit to maintain the reception characteristics of the decoy signal similar to the real signal and enhance the concealment of the interference. Finally, in order to achieve intelligent feedback optimization and the final decoy target in steps Sp5 and Sp6, a strategy based on reinforcement learning is introduced. Let s t Indicates the system status (including the current position, speed, and interference parameters of the drone, etc.), a t represents the action selected by the decoy system at time t (adjusting signal power, adjusting virtual trajectory, changing frequency drift, etc.), r t represents the immediate reward obtained at time t (defined by whether the drone enters the predetermined area, whether it responds to the decoy signal as expected, etc.), then the value function used in reinforcement learning satisfies the Bellman equation:

[0051]

[0052] Where γ is the discount factor, and the optimal strategy π is iteratively solved through repeated interactions (i.e., continuously generating interference, observing drone responses, and updating strategies) *, and dynamically optimize the parameters of the drone decoy trajectory and jamming signal. After the drone enters the predetermined area, the signal recovery mechanism gradually reduces the strength of the pseudo signal and switches to the real navigation signal, successfully completing the task of decoying and jamming the drone's track. The above models and algorithms run through the entire technical solution and cooperate with each other in each step: multi-sensor data fusion provides accurate target state estimation for the jamming strategy, the jamming and decoy signal model ensures seamless connection between the pseudo signal and the real signal, the dynamic Doppler and path prediction model realizes a realistic simulation of the drone's motion characteristics, and reinforcement learning adaptively adjusts various interference parameters in continuous interaction, ultimately achieving efficient and covert navigation deception.

[0053] 1. Environment initialization:

[0054] 1. Deployment of environmental perception module:

[0055] This solution uses radar sensors, infrared sensors, and radio signal receiving equipment to build a multi-sensor perception network. Radar sensors can obtain the position and relative distance of drones in three-dimensional space (x, y, z coordinates); infrared sensors can measure the speed and direction of drones (i.e., heading information); and radio receiving equipment can capture the characteristic data of navigation signals used by drones, navigation protocol types (GPS, GLONASS, Beidou, etc.), receiving frequency bands, signal strengths, and modulation methods, etc. Environmental perception modules are often distributed around areas that need to be protected or controlled. With the help of multi-point deployment and high-precision data fusion, drones in the target area can be detected and identified in a relatively short time, and their initial position, speed, communication or navigation signal characteristics and other key information can be determined. 2. Modeling of drone navigation system: Based on the perceived multi-source data, the navigation system of the drone needs to be modeled to facilitate subsequent interference and deception of its weaknesses or features. Let x k =[x k ,y k ,z k ,v x,k ,v y,k ,v z,k ,…] represents the state vector of the drone at time k (including position and speed, etc.), z k Represents multi-sensor observations (including radar range / angle information, infrared speed and direction, and radio navigation signal strength and frequency offset). The process model of the UAV can be written as

[0056] where F k represents the state transfer matrix, which integrates the kinematic and dynamic characteristics of the UAV; w k is the process noise. The corresponding observation model can be written as

[0057] Among them, Hk is the observation matrix, which maps the UAV state to the sensor’s measurement domain; v k is the observation noise.

[0058] The system uses extended Kalman filtering or unscented Kalman filtering to estimate and update the state space model in real time, thereby obtaining the precise position, speed, heading and other dynamic information of the UAV.

[0059] High-precision data fusion:

[0060] In order to make full use of multi-source sensor information, the system combines the measurement values ​​of each sensor through a multi-sensor data fusion algorithm to eliminate the uncertainty caused by single sensor errors.

[0061] The final output result is the continuous, high-precision positioning and heading estimation of the UAV in the target area. These parameters will be used by subsequent jammers and decoy modules.

[0062] 2. Interference signal generation:

[0063] Multi-band interference module design:

[0064] The jammer module contains multiple programmable RF front ends that can simultaneously operate in multiple satellite navigation frequency bands such as L1, L2, and L5 to match the signal characteristics of different navigation systems.

[0065] Each RF front end can use a high-precision frequency synthesis algorithm to generate a stable frequency interference signal to avoid failure due to unstable signal frequency.

[0066] The adaptive power regulation circuit is used to control the amplitude of the interference signal in order to flexibly match the receiving characteristics of the target UAV.

[0067] Generation of various interference types:

[0068] Pseudo-random noise interference: Generates white noise with a relatively wide bandwidth to drown out or weaken the reception of the drone's real navigation signal.

[0069] Frequency drift interference: Slow or fast random drift around the center frequency, disrupting the drone's phase-locked loop and carrier tracking loop.

[0070] Phase interference: Random or regular interference components are superimposed on the phase to disrupt the synchronization of the drone receiver in the phase domain.

[0071] Pseudo-signal injection signal: a fake navigation signal generated by a simulated satellite signal source, which imitates real ephemeris data, spread spectrum code, timestamp, etc., and directly deceives the drone’s navigation system.

[0072] 3. Mixture of interference signal and real signal:

[0073] If you use s r (t) represents the real navigation signal in the target area, s j (t) represents the interference signal output by the multi-band interference module, and the final signal at the receiving end of the drone can be expressed as

[0074] where α r ,α j is the amplitude weight of the real signal and the interference signal, and n(t) is the environmental noise. By flexibly adjusting α j ,The system can control the intensity of the jamming signal to the critical point of drowning or covering the real signal, ,preparing for subsequent decoy operations.

[0075] 3. Decoy signal injection:

[0076] Multi-protocol fusion module:

[0077] The system needs to generate a "fake" satellite signal that is compatible with the target drone based on the navigation protocol used by the drone and its internal spread spectrum code system.

[0078] The multi-protocol fusion module can simultaneously construct a combination signal of several protocols to prevent the target drone from having multi-mode reception capabilities. At the same time, the carrier frequency, spread spectrum code rate, and ephemeris data format between different protocols all need to be accurately simulated.

[0079] Key parameters of virtual satellite signals:

[0080] Timestamp: The pseudo signal should be highly synchronized with the real navigation signal in the time domain to avoid obvious time jumps that may cause abnormal recognition by the drone receiver.

[0081] Ephemeris data: including forged satellite orbit parameters, satellite clock corrections, etc., so that the drone believes that these "satellites" are available and reliable during the calculation process.

[0082] Multipath delay characteristics: In order to enhance the authenticity of the signal, targeted multipath reflection or delay characteristics can be injected, making it difficult for the drone receiver to distinguish which are spoof signals through simple multipath detection algorithms.

[0083] Injection strategy and power control:

[0084] In order to make the drone gradually trust the fake satellite signals in its navigation solution, the system will perform appropriate overlay or injection in the spectrum and time domain.

[0085] If the real signal is completely overwhelmed too early, the drone’s safety strategy will be triggered; if the interference intensity is not strong enough, it will not be easy for the drone to "lock" on the fake satellite signal.

[0086] Therefore, the multi-protocol fusion module will determine the specific power injection point and injection timing based on the perceived characteristics of the drone receiver, the capture and tracking algorithm it uses, etc.

[0087] 4. Dynamic signal control:

[0088] Doppler effect simulation:

[0089] When a drone is flying in the air, its receiver will produce Doppler frequency shift due to the relative motion of the satellite or interference source. If the pseudo signal does not accurately simulate the Doppler effect, it will be quickly identified as abnormal by the receiver.

[0090] In the dynamic signal control module, the Doppler effect simulation unit calculates the frequency offset based on the relative speed v of the drone and the angle θ of the propagation direction:

[0091] Where f0 is the center frequency and c is the speed of light. doppler (t)-f0 is the frequency drift injected in real time.

[0092] Based on the UAV kinematic model and state estimation results obtained in step Sp1, the real-time path prediction unit can infer the position, speed and azimuth of the UAV at subsequent times (k+1), (k+2), etc. The prediction results will be fed back to the Doppler effect simulation unit and the power control module in real time to update key parameters such as Δf, Δφ and signal injection power, so that the characteristics of the forged signal always match the demodulation results of the UAV's airborne receiver. 3. Enhanced concealment: During the dynamic control process, a small amount of random fine-tuning will be introduced to ensure that the interference signal will not present an overly idealized model distribution in frequency and phase, thereby reducing the risk of being identified by the receiver through a high-order correlation detection algorithm. At the same time, the dynamic control module will also adjust the relative phase change of the pseudo signal within a certain time range according to the current state of the UAV, creating a sense of reality that "the satellite is also in dynamic motion relative to the UAV."

[0093] In the dynamic signal control module, the Doppler effect simulation unit calculates the frequency offset based on the relative speed v of the drone and the angle θ of the propagation direction:

[0094] Where f0 is the center frequency and c is the speed of light. doppler (t)-f0 is the frequency drift injected in real time.

[0095] 5. Intelligent feedback optimization:

[0096] 1. Reinforcement Learning Framework:

[0097] In order to maintain the best deception strategy for drones in a constantly changing environment, this scheme uses reinforcement learning (RL) to dynamically optimize the interference parameters and virtual trajectory guidance methods. t represents the state of the system at time t, including the latest position, speed, received signal quality index and current interference injection parameters of the UAV, a t Represents the action selected by the system at time t, such as "adjusting the interference power", "adjusting the virtual waypoint", "changing the Doppler shift increment", etc. By defining the instant reward r t ”, “whether the drone deviates from the original route”, “whether it is close to the predetermined area”, “whether the drone fault protection is triggered prematurely”, etc., to measure the action a t In status t The degree of superiority or inferiority.

[0098] The value function of reinforcement learning satisfies the Bellman equation:

[0099] In the process of continuous interactive experiments, the system gradually learns the optimal strategy π * , in order to select the best decoy action in different positions and interference environments of the UAV.

[0100] 2. Mathematical model of drone response:

[0101] When conducting reinforcement learning, it is necessary to establish a mathematical model of the drone's response to various interference signals. The change in the drone's heading is closely related to its navigation decoding results. If the false signal indicates that its "target position" is in a certain direction, the onboard flight control system will produce a corresponding turn or speed change. The model is often fitted or estimated based on information such as the characteristics of the drone's flight control system, the trajectory planning method (waypoint-based autopilot), and is updated in real time when compared with the actual observation results.

[0102] Feedback optimization position in the system:

[0103] The reinforcement learning algorithm can run continuously in the background to correct key interference parameters at small and medium sampling frequencies, and does not necessarily need to be updated at extremely high frequencies.

[0104] The intelligent feedback unit provides the system with "self-learning" capabilities, which can accumulate experience in different types of drones and different environmental scenarios, and quickly converge to a set of applicable interference and deception strategies.

[0105] 6. Deception Target Achievement:

[0106] Virtual trajectory generation and guidance:

[0107] After obtaining the UAV mission information, an alternative flight trajectory is designed through the "virtual trajectory generation unit" to gradually guide the UAV to the predetermined area.

[0108] This virtual trajectory can be updated in real time in three-dimensional space, and in conjunction with the dynamic signal control module, the drone navigation can calculate a stable and logical "fake route" and fly towards the controlled area over time.

[0109] Gradually reduce the false signal strength and restore true navigation:

[0110] When the UAV enters the predetermined area, the system begins to weaken the interference power and the intensity of the false signal injection, gradually allowing the UAV receiver to "reacquire" the real satellite signal and smoothly transition to the real navigation state.

[0111] Doing so can avoid navigation failures or process confusion in the drone system at the moment the interference is lifted, and allow it to end the flight in a more natural way after completing the decoy mission.

[0112] Signal synchronization module and recovery mechanism:

[0113] To ensure that there is no obvious jump in the transition from the pseudo signal to the real signal, the signal synchronization module must be synchronized with the real navigation signal with high precision.

[0114] The module contains a high-precision time synchronization unit and a pseudo-signal calibration unit, which continuously adjusts the phase and timestamp of the pseudo-signal through an error correction algorithm to keep it almost consistent with the real navigation signal in terms of τ and Δ.

[0115] When the decoy target is achieved and the system needs to exit the interference, it is only necessary to shut down or weaken the injected pseudo signal power as planned to allow the drone to smoothly return to the real navigation solution result.

[0116] The high-precision UAV position, speed, direction and other information is obtained from environmental initialization, and combined with its navigation protocol type, the foundation for subsequent interference and deception is laid; in the interference signal generation stage, a multi-band RF front end and adaptive power control are used to generate diversified interference that can cover or replace the real navigation signal; fake navigation signals are injected through the multi-protocol fusion module, so that the UAV gradually "locks" the fake satellite or fake beacon during navigation solution, thereby affecting its subsequent track; real-time simulation of Doppler frequency shift and phase is performed in dynamic signal control to ensure that the pseudo-signal is consistent with the UAV's expectations in both frequency and time domains, thereby improving concealment and effectiveness; with the help of intelligent feedback optimization with reinforcement learning as the core, the system is automatically optimized in continuous interaction with the UAV. Adaptively adjust the jamming and decoy parameters to improve the success rate and robustness; finally, in the realization of the decoy target, guide the UAV to the predetermined position through the "virtual track", and then smoothly restore to the real navigation signal to complete the entire decoy and jamming process; the entire process needs to take into account the following technical implementations: high-precision time synchronization and error correction to ensure seamless splicing of the forged satellite signal and the real signal; interference intensity and injection ratio to avoid premature activation of the UAV's protection mechanism or self-destruction program; a deep understanding of the UAV's flight control and navigation algorithms in order to successfully forge "trusted" navigation data at the algorithmic level; concealment, through random small adjustments, multi-path simulation, frequency and phase perturbations, etc., to make the decoy signal difficult to be identified by simple anomaly detection algorithms. Specific embodiment 2:

[0118] Based on the technical solution of the specific embodiment 1, the drone navigation deception method is used for intercepting illegal drones in airport areas, protecting the security of military bases, and testing the anti-interference performance of navigation systems. The following application cases are further given:

[0119] Case 1: In the vicinity of airports, illegal drones may interfere with the normal takeoff and landing of flights and affect the safety of airport operations. Due to the rich electromagnetic environment and strict aviation control requirements in the airport area, a set of refined means is needed to effectively intercept drones while avoiding large-scale interference with civil aviation navigation equipment.

[0120] Global monitoring and rapid identification:

[0121] Radar, infrared and radio sensing equipment are deployed around the airport and near the runway for real-time monitoring of low-altitude flying targets. When a suspected illegal drone is found to enter the control range, the system will immediately lock its position, speed and the navigation protocol used. According to the detected navigation characteristics of the drone, a multi-band interference module is used to interfere with its navigation band in a directional manner to avoid a large-scale impact on other wireless frequencies normally used by the airport (such as aviation communications and civil aviation navigation signals). At the same time, a fake navigation signal is injected to cause the drone's onboard navigation solution to deviate, gradually guiding it from the flight route to a safe open space or wasteland to prevent it from posing a threat to the airport runway and passenger aircraft. During the interception process, the drone can be taken away from the passenger aircraft route through a pre-set "virtual trajectory", and even guided to land or make an emergency landing in a safe area far away from the airport (such as an open area). Compared with traditional "hard destruction" or "direct electronic interference", this is more controllable and less likely to cause secondary harm to ground personnel or key facilities caused by falling objects. After completing the interception of the illegal drone, the system gradually reduces the interference power, allowing surrounding civil aircraft or other unrelated aircraft to continue to use normal navigation signals, thereby maintaining the overall flight safety of the airport.

[0122] Highly targeted deception and jamming methods can avoid harming innocent targets to the greatest extent and reduce interference with civil aviation frequency bands. Combined with multiple sensors and real-time positioning estimation, it can effectively intervene in illegal drones in a short period of time and guide drones through virtual tracks to prevent them from falling randomly and posing a threat to airport operations and ground facilities.

[0123] Case 2: In military bases or sensitive areas, drones not only pose a risk of intelligence leakage, but may also carry weapons and explosives to threaten personnel and facilities inside the base. At this time, unknown or suspicious drones entering the airspace above the base need to be intercepted in a timely, accurate and highly covert manner.

[0124] Military bases often have complete electronic reconnaissance and radar systems, which can be combined with the multi-sensor fusion module of this technical solution to detect and identify low-altitude or ultra-low-altitude drones. The monitoring system determines whether the drone is in a "friendly" or "enemy" "unknown" state, and immediately intervenes in subsequent interference for drones that are not on the authorized list. According to the navigation mode of the enemy drone (GPS, Beidou or other), a multi-band interference module is used to send fake satellite signals and noise interference to it in a time-sharing or synchronous manner, making it unable to accurately identify its own position. Through the multi-protocol fusion module, the real ephemeris information and timestamps are simulated, so that the enemy drone gradually "deviates" or "hover" without knowing it, buying time for the base air defense force's follow-up actions.

[0125] Military drones may have more complex anti-interference capabilities and multiple backup navigation or flight control algorithms. At this time, intelligent feedback mechanisms such as reinforcement learning can be used to continuously observe their performance and adaptively adjust the interference and deception strategies. When it is found that the enemy drone begins to try to switch to inertial navigation or other emergency modes, it can also quickly identify and switch to the corresponding interference method to improve the interception success rate. In the case that the deception method cannot completely guide or force the drone to land, it can be linked with other "hard kill" systems (such as lasers, directed energy weapons, and missile systems). The time and position advantages gained by the deception system can significantly improve the accuracy of subsequent hard destruction methods, avoid unnecessary consumption of the base's air defense forces, and prevent drones from conducting reconnaissance or positioning of important facilities and personnel inside the base to the greatest extent. From early detection to interference, deception, and final disposal, a hierarchical and integrated security protection system is formed, and the false signals imposed on the enemy drones are difficult to distinguish from the real signals, reducing the risk of exposing the location or technical details of one's own interference equipment.

[0126] Case 3: For large aircraft, drones or vehicle equipment, how to maintain positioning and navigation accuracy in a strong interference environment is a key performance indicator. Traditional laboratory tests are mostly static or semi-physical simulations, which are difficult to realistically simulate complex electromagnetic environments and deceptive interference.

[0127] Through the programmable RF front end, various types of interference signals are generated, including broadband noise, narrowband frequency sweep, phase modulation, pseudo-satellite signals, etc., to create an interference situation close to the actual scene. At the same time, virtual satellite arrays with different satellite ephemeris and different altitudes / azimuths can be simulated to examine the anti-spoofing ability of the navigation system under test. In actual tests, in order to observe whether the anti-spoofing algorithm of the airborne receiver under test is effective, pseudo-trajectory can be induced "manually" or "customly". For example, inject specific phase offsets and Doppler frequency shifts into the navigation system under test to see if it can identify anomalies through multipath detection or carrier coherence technology. The solution is deployed in an experimental range or test track, and when the drone (or vehicle) under test is in a moving state, it is continuously interfered and deceived. By comparing the actual motion trajectory of the object under test with the position, speed and other information obtained by the solution, the robustness of the system in a complex interference environment can be evaluated.

[0128] During the test, the original baseband signal, carrier tracking and final solution results of the receiver under test are recorded and compared with the interference parameters output by the decoy system. Detailed data playback and analysis after the test can help researchers quickly identify the weak links in the system and provide a basis for improving the navigation reception algorithm. By injecting real interference and fake signals, the test environment is closer to actual combat or field applications; the interference signal parameters can be accurately controlled, and different batches of tests are comparable; the test is carried out within a controllable range to avoid major loopholes or design defects in the navigation system being discovered in an actual combat environment.

[0129] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0130] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A UAV navigation deception method based on multi-navigation signal interference, characterized by: The method comprises the following steps: Sp1, environmental initialization: including an environmental perception module, which scans the target area, obtains the signal characteristic data of the UAV, including the navigation protocol type, receiving frequency band, signal strength and dynamic flight parameters, and builds a navigation system model of the UAV; Sp2, interference signal generation: including a multi-band interference module, the multi-band interference module generates a pseudo-random noise interference signal, a frequency drift interference signal, a phase interference signal and a pseudo signal injection signal, and uses a simulated satellite signal source to generate a fake navigation signal; Sp3, decoy signal injection: including a multi-protocol fusion module, which constructs a multi-protocol combination signal that matches the navigation protocol used by the target UAV, and injects a virtual satellite signal by adjusting the signal power and transmission angle; Sp4, dynamic signal control: including a dynamic signal control module and a signal synchronization module, the dynamic signal control module adjusts the intensity, frequency and phase parameters of the pseudo signal in real time to simulate the dynamic Doppler effect; Sp5, Intelligent Feedback Optimization: Use artificial intelligence algorithms to analyze the response of the drone navigation system in real time and dynamically optimize the decoy signal parameters; Sp6. Target deception is achieved: The UAV is guided to deviate from the original flight mission trajectory through a virtual trajectory, and the pseudo signal strength is gradually reduced after the UAV enters the predetermined area to restore the real navigation signal.

2. The method for UAV navigation deception based on multi-navigation signal interference according to claim 1 is characterized in that: The multi-band jammer module includes multiple programmable RF front ends for achieving synchronous jamming of the L1, L2 and L5 bands, and optimizes signal strength to match the target drone reception characteristics through an adaptive power regulation circuit.

3. The method for UAV navigation deception based on multi-navigation signal interference according to claim 1 is characterized in that: The multi-protocol fusion module adopts a multi-protocol parsing and reconstruction algorithm to generate a forged signal by real-time decoding and simulation of the navigation protocol of the target UAV. The forged signal includes a timestamp, ephemeris data, and multi-path delay data.

4. The method for UAV navigation deception based on multi-navigation signal interference according to claim 1 is characterized in that: The dynamic signal control module includes a Doppler effect simulation unit and a real-time path prediction unit, and the Doppler effect simulation unit and the real-time path prediction unit calculate the frequency drift of the pseudo signal based on the flight speed and heading of the target UAV.

5. The method for UAV navigation deception based on multi-navigation signal interference according to claim 1 is characterized in that: The intelligent feedback optimization step adjusts the injection position, intensity and trajectory deviation parameters of the decoy signal based on the reinforcement learning algorithm and environmental perception data, and predicts the path change trend of the drone by establishing a mathematical model of the drone's response.

6. The method for UAV navigation deception based on multi-navigation signal interference according to claim 1 is characterized in that: The environmental perception module achieves high-precision positioning and navigation status monitoring through a multi-sensor data fusion algorithm. The environmental perception module includes a radar sensor, an infrared sensor and a radio signal receiving device. The radar sensor is used to obtain the three-dimensional spatial position of the drone, the infrared sensor is used to detect the speed and direction of the drone, and the radio signal receiving device is used to capture the characteristic parameters of the navigation signal.

7. The method for UAV navigation deception based on multi-navigation signal interference according to claim 1 is characterized in that: The decoy target realization includes constructing a dynamically adjusted flight trajectory through a virtual trajectory generation unit.

8. The method for UAV navigation deception based on multi-navigation signal interference according to claim 1 is characterized in that: The signal synchronization module includes a high-precision time synchronization unit and a pseudo-signal calibration unit, and the signal synchronization module is synchronized with the time of the real navigation signal, and combined with the error correction algorithm to achieve the fusion of the decoy signal and the real signal.

9. The method for UAV navigation deception based on multi-navigation signal interference according to any one of claims 1 to 8, characterized in that: The drone navigation spoofing method is used for intercepting illegal drones in airport areas, protecting the security of military bases, and testing the anti-interference performance of navigation systems.

Images