A Dynamic Anti-Jamming Deception Navigation Method and System for Unmanned Aerial Vehicles

Abstract

The present invention relates to the field of anti-spoofing navigation technology, and discloses a dynamic anti-jamming spoofing navigation method and system for an unmanned aerial vehicle, including: acquiring signals of a satellite navigation system during the flight of the unmanned aerial vehicle, detecting and identifying potential interference signals; adjusting filter parameters in real time according to the motion state of the unmanned aerial vehicle to filter out noise and jumping signals; when the signals of the satellite navigation system are severely interfered, fusing the signals of the satellite navigation system with the data of an inertial measurement unit to maintain short-term navigation of the unmanned aerial vehicle and achieve dynamic anti-jamming spoofing of the unmanned aerial vehicle. By adopting multi-dimensional signal detection and analysis such as multi-frequency multi-antenna reception, time propagation error, power estimation, and pseudorange consistency, the present invention can accurately identify and filter interference and spoofing signals. When the satellite navigation signals are severely interfered, short-term navigation is carried out through IMU data to ensure the flight stability and navigation continuity of the unmanned aerial vehicle in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-spoofing navigation, and specifically to a dynamic anti-jamming spoofing navigation method and system for unmanned aerial vehicles (UAVs). Background Art

[0002] With the rapid development and industrial promotion of UAV technology, UAVs are increasingly widely used in multiple fields. Especially in traditional tasks that rely on manual operation such as power line inspection, the autonomous inspection of UAVs not only effectively reduces labor costs but also enables a comprehensive inspection of equipment, thus significantly improving the inspection efficiency. However, due to the vulnerability of satellite navigation signals, the satellite navigation system relied on by UAVs is extremely vulnerable to interference and spoofing signals during operation. These interferences may cause the UAV's heading to deviate, and in severe cases, it may be misled by false signals, resulting in significant economic losses. In recent years, interference and spoofing problems have gradually become one of the main obstacles affecting the operation efficiency and safety of UAVs. Therefore, the research on anti-jamming and anti-spoofing navigation technology for UAVs is of great significance for ensuring the safety of UAVs and improving work efficiency.

[0003] Currently, there have been a large number of studies on GPS anti-spoofing technology for static scenarios, including antenna improvement technology, measurement consistency methods, and phase-locked loop improvement schemes. These methods have certain anti-spoofing performance in static scenarios. However, due to the dynamic nature of UAVs, these technologies are not applicable to dynamic scenarios. Especially when it comes to the positioning and navigation requirements of UAVs flying at high speeds and in complex environments, traditional static algorithms cannot adapt to the changes in dynamic observations during the filtering process, resulting in a decrease in navigation accuracy and instability in the solution process.

[0004] Regarding the research on GPS positioning in dynamic environments, there have been some methods that combine adaptive Kalman filtering and extended Kalman filtering to improve positioning accuracy and tracking performance, and good stability has been achieved in some scenarios. However, there is still a lack of a navigation anti-spoofing technology that can ensure positioning accuracy in a high-dynamic environment while taking into account the stability of the solution. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that existing anti-spoofing technologies are not applicable to dynamic scenarios and cannot meet the positioning and navigation requirements of UAVs flying at high speeds and in complex environments.

[0007] To solve the above technical problems, the present invention provides the following technical solution: A dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle, comprising: acquiring signals of a satellite navigation system during the flight of the unmanned aerial vehicle, detecting and identifying potential interference signals; adjusting filter parameters in real time according to the motion state of the unmanned aerial vehicle to filter out noise and jumping signals; when the signals of the satellite navigation system are severely interfered, fusing the signals of the satellite navigation system with the data of an inertial measurement unit to maintain short-term navigation of the unmanned aerial vehicle and achieve dynamic anti-jamming spoofing of the unmanned aerial vehicle.

[0008] As a preferred embodiment of the dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle according to the present invention, wherein: the acquiring of signals of the satellite navigation system during the flight of the unmanned aerial vehicle includes receiving satellite signals from different frequency bands simultaneously through antennas at different positions of the unmanned aerial vehicle and measuring the angles of arrival of the signals.

[0009] As a preferred embodiment of the dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle according to the present invention, wherein: the detecting and identifying of potential interference signals includes identifying interference signals and spoofing signals by analyzing the power intensity and arrival time of the received navigation signals: calculating the time propagation error according to the difference between the actual reception time and the theoretical arrival time of the navigation signal; calculating the power error by comparing the actual power value of the received signal with the theoretical attenuation model of the distance from the navigation satellite to the unmanned aerial vehicle; measuring the pseudorange of multiple navigation satellites simultaneously and calculating the pseudorange difference.

[0010] As a preferred embodiment of the dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle according to the present invention, wherein: after the detecting and identifying of potential interference signals, it further includes setting weights of the time propagation error, power error and pseudorange difference according to the flight mission of the unmanned aerial vehicle, obtaining a first result through weighted synthesis, and evaluating according to the first result; presetting a first interval, a second interval and a third interval to compare with the first result to determine the triggering of an action; the action includes no action, a first action and a second action; the triggering includes, when the first result belongs to the first interval, judging as no interference and executing no action; when the first result belongs to the second interval, judging as slight interference and executing the first action; when the first result belongs to the third interval, judging as severe interference and executing the second action.

[0011] As a preferred embodiment of the dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle according to the present invention, wherein: the adjusting of filter parameters includes acquiring speed and acceleration information in real time through the inertial measurement unit of the unmanned aerial vehicle and adjusting the weight of process noise according to the speed and acceleration information; dynamically adjusting the measurement noise covariance matrix by monitoring the quality index of the navigation signal; coping with signal jumping and noise interference by adjusting the process noise covariance matrix according to the pseudorange and speed data in the navigation signal.

[0012] As a preferred embodiment of the dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle (UAV) according to the present invention, wherein: the data fusion includes that the inertial measurement unit (IMU) obtains the motion state data of the UAV in real time by integrating an accelerometer and a gyroscope, including three-axis acceleration and angular velocity data. When a second action is triggered, the signals of the satellite navigation system and the IMU data are fused through a Kalman filter; a state vector is set including the position, velocity, and attitude information of the UAV; taking the three-axis acceleration and angular velocity output by the IMU as input quantities, calculating the velocity of the UAV and relative displacement ; predicting the state of the UAV at the next moment using the acceleration and angular velocity information of the IMU; continuously detecting the signals of the satellite navigation system. If there are signals of the satellite navigation system belonging to the first interval, the signals of the satellite navigation system are used to provide absolute position information and velocity information for state correction, and the fused state vector is calculated .

[0013] As a preferred embodiment of the dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle (UAV) according to the present invention, wherein: the short-term navigation of the UAV includes that when a second action is triggered, it switches to the IMU navigation mode, calculates the velocity of the UAV and relative displacement , and calculates the attitude angle of the UAV through the angular velocity of the UAV to maintain the attitude stability of the UAV; using the velocity of the UAV and relative displacement to continue flying along a predetermined flight path to maintain navigation continuity within a short period of time; using the fused state vector to correct the cumulative error of the IMU navigation mode, and switching back to the satellite navigation mode when the signals of the satellite navigation system are restored.

[0014] A dynamic anti-jamming spoofing navigation system for an unmanned aerial vehicle (UAV) adopting any of the methods according to the present invention, wherein: a signal processing module acquires the signals of the satellite navigation system received by the UAV, and identifies and filters spoofing signals based on time propagation error, power estimation, and pseudorange consistency verification; an adaptive filtering module performs Kalman filtering on the signals, and adjusts the filter parameters in real time according to the dynamic characteristics of the UAV to optimize the navigation accuracy; a data fusion module switches to the IMU navigation mode when the signals of the satellite navigation system are severely interfered, maintains the short-term navigation of the UAV, and corrects the cumulative error through the fused state vector to achieve dynamic anti-jamming spoofing of the UAV.

[0015] A computer device, comprising: a memory and a processor; the memory stores a computer program, including: steps of implementing the method according to any one of the present inventions when the processor executes the computer program.

[0016] A computer-readable storage medium, on which a computer program is stored, including: steps of implementing the method according to any one of the present inventions when the computer program is executed by a processor.

[0017] Advantages of the present invention: By adopting multi-dimensional signal detection and analysis such as multi-frequency multi-antenna reception, time propagation error, power estimation, and pseudorange consistency, the present invention can accurately identify and filter interference and spoofing signals. At the same time, the adaptive Kalman filtering technology is used to adjust the filter parameters in real time according to the motion state of the unmanned aerial vehicle, improving the navigation accuracy. When the satellite navigation signal is severely interfered, short-term navigation is performed through IMU data to ensure the flight stability and navigation continuity of the unmanned aerial vehicle in a complex environment. The fused state vector effectively corrects the IMU cumulative error, enhancing the anti-interference and anti-spoofing capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0019] Figure 1 It is the overall flowchart of a dynamic anti-interference and anti-spoofing navigation method for an unmanned aerial vehicle provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0021] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides a dynamic anti-interference and anti-spoofing navigation method for an unmanned aerial vehicle, including:

[0022] In S1: Obtain the signals of the satellite navigation system during the flight of the unmanned aerial vehicle, and detect and identify potential interference signals.

[0023] In S2: Adjust the filter parameters in real time according to the motion state of the UAV to filter out noise and jump signals.

[0024] In S3: When the signal of the satellite navigation system is severely interfered, fuse the signal of the satellite navigation system with the data of the inertial measurement unit to maintain the short-term navigation of the UAV and achieve the dynamic anti-jamming deception of the UAV.

[0025] Among them, the signal of the satellite navigation system can be the signals of multiple satellite navigation systems (such as GNSS, Beidou), and a dual-antenna or multi-antenna receiving module is used to receive the signals. The antennas are installed at different positions of the UAV to provide suppression of the signal multipath effect and achieve effective detection of interference signals.

[0026] It should be noted that the multipath effect refers to the navigation signal reaching the receiving antenna after being reflected by buildings, mountains, etc., resulting in signal path delay and deviation.

[0027] Furthermore, by analyzing the power intensity and arrival time of the received navigation signal, identify interference signals and spoofing signals: calculate the time propagation error according to the difference between the actual reception time and the theoretical arrival time of the navigation signal.

[0028] Specifically, record the actual reception time of each navigation satellite signal and compare it with the theoretical propagation time. The theoretical propagation time can be calculated according to the current estimated position of the UAV and the known position of the satellite (through ephemeris data). Propagation time error = actual reception time - theoretical time (calculated according to the speed of light and the distance between the navigation satellite and the UAV). If this error exceeds a predetermined threshold, it indicates that there is an abnormal propagation path, which may be caused by multipath effect or malicious interference.

[0029] Compare the actual power value of the received signal with the theoretical attenuation model of the distance from the navigation satellite to the UAV to calculate the power error. According to the classical free space propagation model, the power of the navigation signal shows a certain attenuation as the distance increases. By comparing the actual power value of the received signal with the theoretical attenuation model of the distance from the navigation satellite to the UAV, power anomalies can be detected.

[0030] Specifically, the intensity of the navigation signal will attenuate as the distance between the satellite and the UAV increases, and this attenuation follows the free space propagation model:

[0031] ,

[0032] Among them, is the signal power received by the UAV; is the signal power transmitted by the satellite; is the wavelength of the signal; is the distance from the satellite to the UAV.

[0033] The system obtains the distance between the navigation satellite and the UAV in real time , according to the pseudorange, applying the free space propagation model, calculates the theoretical signal power that the UAV should receive at this distance , through the receiving module of the UAV, measures the actual signal power received , compares the measured actual power value with the theoretical power value calculated by the model:

[0034] ,

[0035] where, represents the power error.

[0036] At the same time, the pseudoranges of multiple navigation satellites are measured to calculate the pseudorange difference.

[0037] Specifically, the pseudorange is an estimated value of the distance between the receiver and the navigation satellite, and is calculated by measuring the propagation time of the signal from the satellite to the receiver:

[0038] ,

[0039] where, ρ represents the pseudorange; represents the time when the signal arrives at the receiver; represents the time when the signal is emitted from the satellite; represents the speed of light.

[0040] In the pseudorange consistency detection, the system will simultaneously receive the signals of multiple navigation satellites, calculate the pseudorange values between each satellite and the UAV, and calculate the pseudorange difference by comparing the pseudorange data of multiple satellites:

[0041] ,

[0042] where, represents satellite and satellite 's pseudorange difference; represents the pseudorange of satellite ; represents the pseudorange of satellite .

[0043] Furthermore, after detecting and identifying potential interference signals, it further includes setting weights for the time propagation error, power error, and pseudorange difference according to the flight mission of the UAV, obtaining a first result through weighted synthesis, and evaluating according to the first result.

[0044] Specifically, the first result is calculated by the weighted comprehensive evaluation method, and weights are set for the time propagation error, power error, and pseudorange difference respectively. The magnitude of the weights can be dynamically adjusted according to different environments, flight missions, or UAV navigation requirements. For example: in an open suburban environment, the pseudorange difference may be more sensitive, so a larger weight can be given; in the city, the power error and time propagation error are more affected by buildings, so higher weights can be given.

[0045] During actual flight, different types of errors have different degrees of influence on the navigation system. The time propagation error is more prominent in scenarios with severe multipath effects, while in highly open environments, pseudorange consistency detection may be more important. The weighted comprehensive evaluation can reasonably allocate the weights of errors according to these different situations, improving the accuracy of overall judgment.

[0046] The first interval, second interval, and third interval are preset and compared with the first result to determine the triggering of the action.

[0047] The actions include no action, the first action, and the second action;

[0048] The triggering includes,

[0049] When the first result belongs to the first interval, it is judged as no interference and no action is executed;

[0050] When the first result belongs to the second interval, it is judged as slight interference and the first action is executed;

[0051] When the first result belongs to the third interval, it is judged as severe interference and the second action is executed.

[0052] Among them, the first interval, second interval, and third interval are continuous score ranges. According to the score size, they are the first interval, second interval, and third interval from small to large respectively. The first result is evaluated and assigned a value to be compared with the three intervals.

[0053] When the first result belongs to the first interval, it is judged as no interference and no action is executed. At this time, there may be slight interference, such as small-scale power fluctuations or time propagation errors, but this usually does not affect the navigation accuracy and does not require excessive intervention. The status quo can be maintained.

[0054] When the first result belongs to the second interval, it is judged as a minor interference and the first action is executed. When the comprehensive error belongs to this interval, it indicates that there is a certain degree of interference or spoofing signal in the environment. At this time, the first action can be executed, that is, to activate a more powerful anti-interference and anti-spoofing strategy. For example: increase the dynamic response speed of the filter, enhance the response ability to time propagation error and pseudorange anomaly; enable the integrated navigation mode of IMU (Inertial Measurement Unit) and GNSS, and let the IMU data provide navigation support in a short time to cope with possible interference or spoofing signals.

[0055] When the first result belongs to the third interval, it is judged as a severe interference and the second action is executed. When the comprehensive error reaches the third interval, it indicates that there is a severe interference or spoofing signal. At this time, the second action can be executed, such as: switching to the IMU navigation mode, restricting the automatic navigation function of the UAV, and stopping relying on the GNSS signal, and performing short-term autonomous navigation through the IMU or other sensors until the interference source disappears or the UAV returns to a safe area; sending out a warning signal to notify the ground control center that the UAV may encounter a severe spoofing signal and suggesting manual intervention or avoidance operations.

[0056] Furthermore, the adjustment of the filter parameters includes obtaining the speed and acceleration information in real time through the inertial measurement unit of the UAV, and adjusting the weight of the process noise according to the speed and acceleration information. When moving at high speed, the signal changes greatly, and the filter needs to be more sensitive to signal jumps, so the weight of the process noise is increased; when moving at a relatively low speed, the signal changes smoothly, and the weight of the process noise can be appropriately reduced to enhance stability.

[0057] By monitoring the quality index of the navigation signal, the measurement noise covariance matrix is dynamically adjusted. When the UAV encounters different signal interferences (such as multipath effect or occlusion) during flight, the measurement noise will fluctuate accordingly. The adaptive Kalman filter can dynamically adjust the RRR by monitoring the quality index of the navigation signal (such as signal-to-noise ratio, signal strength, etc.), and enhance the signal processing ability of the filter in an interference environment.

[0058] According to the pseudorange and speed data in the navigation signal, by adjusting the process noise covariance matrix, it can cope with signal jumps and noise interference. When the pseudorange data or speed data in the navigation signal shows abnormal changes in a short time, the filter can enhance the processing ability of signal jumps by adjusting the process noise covariance matrix, quickly filter out abnormal data points, and prevent them from affecting the navigation system.

[0059] Furthermore, the data fusion includes that the inertial measurement unit obtains the motion state data of the UAV in real time by integrating the accelerometer and gyroscope, including the three-axis acceleration and angular velocity data. When the second action is triggered, the signals of the satellite navigation system and the data of the inertial measurement unit are fused through a Kalman filter, which specifically includes sub-steps C1 - C4:

[0060] In C1: Set the state vector including the position, velocity and attitude information of the UAV:

[0061] ,

[0062] wherein, is the three-dimensional coordinate of the UAV; is the three-dimensional velocity; is the attitude angle (pitch angle, roll angle and yaw angle) of the UAV.

[0063] In C2: Take the three-axis acceleration and angular velocity output by the inertial measurement unit as input quantities, and calculate the velocity and relative displacement of the UAV. Since the IMU data is acceleration and angular velocity, the velocity and position of the UAV can be obtained by integrating these data, which is expressed as:

[0064] ,

[0065] ,

[0066] wherein, represents the velocity at time ; represents the position at time ; represents the sampling time interval; represents the acceleration.

[0067] In C3: Use the acceleration and angular velocity information of the IMU to predict the state of the UAV at the next moment:

[0068] ,

[0069] wherein, represents the state of the UAV at the next moment; represents the state transition matrix; represents the input matrix; represents the acceleration and angular velocity output by the IMU.

[0070] In C4: Continuously detect the signals of the satellite navigation system. If there are signals of the satellite navigation system belonging to the first interval, use the signals of the satellite navigation system to provide absolute position information and velocity information, perform state correction, and calculate the fused state vector. :

[0071] ,

[0072] Among them, is the Kalman gain; is the GNSS measurement value; is the measurement matrix; is the GNSS measurement noise covariance.

[0073] It should be noted that the GNSS signal provides absolute position information and velocity information with high long-term accuracy, but may be unstable or unavailable when interfered. Therefore, only when the signals of the satellite navigation system belonging to the first interval are detected, the update formula is used for error correction, and the IMU navigation is mainly used at other times.

[0074] Furthermore, the short-term navigation of the UAV includes that when the second action is triggered, switch to the inertial measurement unit navigation mode, calculate the velocity and relative displacement of the UAV, and calculate the attitude angle of the UAV through the angular velocity of the UAV to keep the attitude of the UAV stable.

[0075] Utilize the velocity and relative displacement of the UAV to continue flying along the predetermined flight path to maintain the navigation continuity within a short time.

[0076] Use the fused state vector to correct the cumulative error of the inertial measurement unit navigation mode, and switch back to the satellite navigation mode when the signals of the satellite navigation system are restored.

[0077] If the above functions are implemented in the form of software function units 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 the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0078] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0079] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as necessary, and then storing it in a computer memory.

[0080] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0081] Embodiment 2. In an exemplary embodiment, a dynamic anti-jamming spoofing navigation system for an unmanned aerial vehicle (UAV) is also provided, including a signal processing module that acquires the signals of the satellite navigation system received by the UAV and identifies and filters spoofing signals based on time propagation error, power estimation, and pseudorange consistency verification; an adaptive filtering module that performs Kalman filtering on the signals and adjusts the filter parameters in real time according to the dynamic characteristics of the UAV to optimize the navigation accuracy; and a data fusion module that switches to the inertial measurement unit navigation mode when the signals of the satellite navigation system are severely interfered, maintains the short-term navigation of the UAV, and corrects the cumulative error through the fused state vector to achieve the dynamic anti-jamming spoofing of the UAV.

[0082] Among them, the signal processing module further includes a dual-antenna or multi-antenna receiving module. By arranging antennas at different positions, it is possible to measure the angle of arrival of the signals, which helps to judge the true position of the signal source and effectively filter false signals.

[0083] Embodiment 3. Hereinafter, an embodiment of the present invention provides a dynamic anti-jamming spoofing navigation method for an unmanned aerial vehicle.

[0084] In this embodiment, a UAV equipped with a dual-antenna receiving module and an inertial measurement unit (IMU) is used for testing the anti-jamming spoofing of the navigation system. The UAV flies in multiple flight environments (open fields and urban environments), receives signals from satellite navigation systems such as GNSS and Beidou, and through implementing the technical solutions of the present invention, detects and filters interference signals, adjusts the filter parameters, and performs data fusion and short-term navigation operations in case of severe interference.

[0085] The beneficial effects of the present invention are verified through different flight environments. During the flight, the system records the navigation signals of each satellite, and uses a tunnel to simulate the situation where the navigation signals are severely interfered. The experimental data is shown in Table 1.

[0086] Table 1 Experimental data table

[0087] ,

[0088] From the above experimental data, the performance of the present invention in different flight environments can be seen. Among them, in Environment 1, both the time propagation error and the power error are maintained within a small range (3.2 ns and 2 dB), indicating that in this environment, the navigation signal is little interfered, and the pseudorange difference is almost non-existent (0.5 m). In this scenario, the anti-interference strategy of the present invention can effectively maintain the accuracy of the signal without complex anti-interference processing, and the system shows high stability.

[0089] In Environments 2 (urban) and 3 (tunnel), the navigation signal is significantly affected by multipath effects and interference sources. In Environment 2, the time propagation error reaches 15.8 ns, the power error also increases significantly (7 dB), and the pseudorange difference reaches 5.2 meters. Based on these data, the system determines the existence of severe interference and triggers the first action, that is, through the fusion navigation of IMU and GNSS, successfully maintaining the navigation continuity in a short time and avoiding the loss of the navigation signal. In the tunnel scenario, the interference is more severe, the pseudorange difference reaches 7.8 meters, the system switches to the IMU-dominated navigation mode, and performs calibration after the GNSS signal is restored to ensure that the drone can successfully complete the flight mission.

[0090] In addition, in the flight data of Environments 5 and 6, the system detects moderate interference, especially the pseudorange differences reach 3.6 meters and 2.1 meters respectively, but do not exceed the interference determination threshold of the system. Therefore, the system executes the first action, increasing the dynamic response speed of the filter to ensure sufficient response to the slight interference in these environments.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A dynamic anti-interference deception navigation method for unmanned aerial vehicles, characterized in that: include: Acquire satellite navigation system signals during drone flight, detect and identify potential interference signals; Acquiring the signal of the satellite navigation system during the flight of the drone includes simultaneously receiving satellite signals from different frequency bands through antennas at different positions of the drone, and measuring the angle of arrival of the signal; The detecting and identifying potential interference signals includes identifying interference signals and spoofing signals by analyzing the power strength and arrival time of the received navigation signals; Calculate the time propagation error based on the difference between the actual reception time and the theoretical arrival time of the navigation signal; The power error is calculated by comparing the actual power value of the received signal with the theoretical attenuation model of the distance from the navigation satellite to the drone; Simultaneously measure the pseudoranges of multiple navigation satellites and calculate the pseudorange differences; Adjust the filter parameters in real time according to the motion state of the drone to filter out noise and jump signals; When the signal of the satellite navigation system is slightly interfered with, the signal of the satellite navigation system is fused with the data of the inertial measurement unit to maintain the short-term navigation of the UAV and realize the dynamic anti-interference deception of the UAV; The adjusting the filter parameters includes obtaining speed and acceleration information in real time through an inertial measurement unit of the UAV, and adjusting the weight of the process noise according to the speed and acceleration information; By monitoring the quality indicators of the navigation signal, the measurement noise covariance matrix is ​​dynamically adjusted; According to the pseudo-range and velocity data in the navigation signal, the process noise covariance matrix is ​​adjusted to deal with signal jumps and noise interference; After detecting and identifying the potential interference signal, the method further includes setting weights of the time propagation error, the power error and the pseudorange difference according to the flight mission of the UAV, performing weighted synthesis to obtain a first result, and performing evaluation according to the first result; The preset first interval, second interval and third interval are compared with the first result to determine the triggering of the action; The actions include no action, a first action, and a second action; The triggering includes, when the first result belongs to the first interval, judging that there is no interference, and performing no action; When the first result belongs to the second interval, it is determined to be a slight interference, and the first action is performed; When the first result belongs to the third interval, it is judged as severe interference, and the second action is performed; The first action includes fusing the signal of the satellite navigation system with the data of the inertial measurement unit to maintain the short-term navigation of the UAV; The second action includes limiting the automatic navigation of the drone, stopping reliance on satellite navigation system signals, and enabling the inertial measurement unit for short-term autonomous navigation.

2. The dynamic anti-interference deception navigation method for unmanned aerial vehicles according to claim 1, characterized in that: The data fusion includes that the inertial measurement unit acquires the motion state data of the drone in real time through an integrated accelerometer and a gyroscope, including three-axis acceleration and angular velocity data, and when the first action is triggered, the signal of the satellite navigation system and the inertial measurement unit data are fused through a Kalman filter; Set the state vector X to contain the position, velocity and attitude information of the drone; The three-axis acceleration and angular velocity output by the inertial measurement unit are used as input to calculate the velocity v(t) and relative displacement p(t) of the drone. Use the acceleration and angular velocity information of the IMU to predict the state of the drone at the next moment; The signal of the satellite navigation system is continuously detected. If there is a signal of the satellite navigation system belonging to the second interval, the signal of the satellite navigation system is used to provide absolute position information and speed information, perform state correction, and calculate the fused state vector X(t).

3. The dynamic anti-interference deception navigation method for unmanned aerial vehicles according to claim 2, characterized in that: The short-term navigation of the UAV includes, when the second action is triggered, switching to the inertial measurement unit navigation mode, calculating the velocity v(t) and relative displacement p(t) of the UAV, and calculating the attitude angle of the UAV through the angular velocity of the UAV to keep the attitude of the UAV stable; Use the speed v(t) and relative displacement p(t) of the UAV to continue flying along the predetermined flight path, maintaining navigation continuity in a short period of time; The fused state vector X(t) is used to correct the accumulated error of the inertial measurement unit navigation mode, and the satellite navigation mode is switched back when the signal of the satellite navigation system is restored.

4. A dynamic anti-interference deception navigation system for unmanned aerial vehicles using any of the methods of claims 1 to 3, characterized in that: include, The signal processing module obtains the signal of the satellite navigation system received by the drone, identifies and filters the deceptive signal based on time propagation error, power estimation and pseudo-range consistency check; The adaptive filtering module performs Kalman filtering on the signal and adjusts the filter parameters in real time according to the dynamic characteristics of the drone to optimize the navigation accuracy. The data fusion module switches to the inertial measurement unit navigation mode when the signal of the satellite navigation system is severely interfered with, maintains the short-term navigation of the UAV, and corrects the accumulated error through the fused state vector to achieve dynamic anti-interference deception of the UAV.

5. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the dynamic anti-interference deception navigation method for an unmanned aerial vehicle as described in any one of claims 1-3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the dynamic anti-interference deception navigation method for a drone as described in any one of claims 1-3 are implemented.

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