Dynamic anti-interference deception navigation method and system for unmanned aerial vehicle

Abstract

The invention relates to the technical field of anti-deception navigation, and discloses a dynamic anti-deception navigation method and system for an unmanned aerial vehicle, and the method comprises the steps: obtaining a signal of a satellite navigation system in the flight process of the unmanned aerial vehicle, and detecting and identifying a potential interference signal; adjusting filter parameters in real time according to the motion state of the unmanned aerial vehicle, and filtering noise and jump signals; when the signal of the satellite navigation system is seriously interfered, the signal of the satellite navigation system and the data of the inertial measurement unit are subjected to data fusion, short-time navigation of the unmanned aerial vehicle is maintained, and dynamic anti-interference deception of the unmanned aerial vehicle is realized. According to the method, interference and deception signals can be accurately identified and filtered by adopting multi-dimensional signal detection and analysis of multi-frequency multi-antenna receiving, time propagation error, power estimation, pseudo-range consistency and the like. When satellite navigation signals are seriously interfered, short-time navigation is carried out through IMU data, and it is ensured that the unmanned aerial vehicle keeps flight stability and navigation continuity in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of anti-deception navigation technology, and in particular to a dynamic anti-interference deception navigation method and system for unmanned aerial vehicles. Background Art

[0002] With the rapid development and industrialization of drone technology, drones are increasingly used in many fields, especially in tasks that traditionally rely on manual operations, such as power inspection. Autonomous inspection by drones not only effectively reduces labor costs, but also allows for all-round inspection of equipment, thereby significantly improving inspection efficiency. However, due to the fragility of satellite navigation signals, the satellite navigation system that drones rely on is extremely susceptible to interference and deception signals during operation. These interferences may cause the drone to deviate from its course, or even be misled by false signals in severe cases, resulting in significant economic losses. In recent years, interference and deception issues have gradually become one of the main obstacles to the efficiency and safety of drone operations. Therefore, research on anti-interference and anti-deception navigation technology for drones is of great significance for ensuring drone safety and improving work efficiency.

[0003] At present, there have been a lot of studies on GPS anti-spoofing technology for static scenes, including antenna improvement technology, measurement consistency method and phase-locked loop improvement scheme. These methods have certain anti-spoofing performance in static scenes. However, due to the dynamic nature of drones, these technologies are not suitable for dynamic scenes, especially when the positioning and navigation needs of drones in high-speed flight and complex environments. Traditional static algorithms cannot adapt to the changes in dynamic observation values ​​during the filtering process, resulting in reduced navigation accuracy and unstable solution process.

[0004] In the study of GPS positioning in dynamic environments, some methods have been used to improve positioning accuracy and tracking performance by combining adaptive Kalman filtering and extended Kalman filtering, and have achieved good stability in some scenarios. However, there is still a lack of navigation anti-spoofing technology that can ensure positioning accuracy in highly dynamic environments while taking into account solution stability. Summary of the invention

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

[0006] Therefore, the technical problem solved by the present invention is that the existing anti-spoofing technology is not suitable for dynamic scenes and cannot meet the positioning and navigation requirements of drones in high-speed flight and complex environments.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a dynamic anti-interference deception navigation method for unmanned aerial vehicles, comprising: obtaining the signal of the satellite navigation system during the flight of the unmanned aerial vehicle, detecting and identifying potential interference signals; adjusting the filter parameters in real time according to the motion state of the unmanned aerial vehicle to filter out noise and jump signals; when the signal of the satellite navigation system is severely interfered with, the signal of the satellite navigation system is data-fused with the inertial measurement unit data to maintain the short-term navigation of the unmanned aerial vehicle, thereby realizing dynamic anti-interference deception of the unmanned aerial vehicle.

[0008] As a preferred solution of the dynamic anti-interference deception navigation method for unmanned aerial vehicles described in the present invention, the acquisition of the signal of the satellite navigation system during the flight of the unmanned aerial vehicle includes simultaneously receiving satellite signals from different frequency bands through antennas at different positions of the unmanned aerial vehicle, and measuring the angle of arrival of the signal.

[0009] As a preferred solution of the dynamic anti-interference deception navigation method for unmanned aerial vehicles described in the present invention, the detection and identification of potential interference signals includes: identifying interference signals and deception signals by analyzing the power strength and arrival time of the received navigation signal: 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; and simultaneously measuring the pseudoranges of multiple navigation satellites to calculate the pseudorange differences.

[0010] As a preferred solution of the dynamic anti-interference deception navigation method for unmanned aerial vehicles described in the present invention, wherein: after the detection and identification of potential interference signals, it also includes setting weights of time propagation error, power error and pseudorange difference according to the flight mission of the unmanned aerial vehicle, weighted synthesis to obtain a first result, 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 that there is no interference, and executing no action; when the first result belongs to the second interval, judging that it is slight interference, and executing the first action; when the first result belongs to the third interval, judging that it is severe interference, and executing the second action.

[0011] As a preferred solution of the dynamic anti-interference deception navigation method for unmanned aerial vehicles described in the present invention, the adjustment of filter parameters includes: obtaining 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; and coping with signal jumps 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 solution of the dynamic anti-interference deception navigation method for unmanned aerial vehicles described in the present invention, the data fusion includes: the inertial measurement unit obtains the motion state data of the unmanned aerial vehicle in real time through an integrated accelerometer and a gyroscope, including three-axis acceleration and angular velocity data, and when the second action is triggered, the signal of the satellite navigation system and the inertial measurement unit data are fused through a Kalman filter; the state vector is set Contains the position, speed and attitude information of the drone; uses the three-axis acceleration and angular velocity output by the inertial measurement unit as input to calculate the speed of the drone and relative displacement ; Use the acceleration and angular velocity information of the IMU to predict the state of the drone at the next moment; Continuously detect the signal of the satellite navigation system. If there is a signal of the satellite navigation system belonging to the first interval, use the signal of the satellite navigation system to provide absolute position information and velocity information, perform state correction, and calculate the fused state vector .

[0013] As a preferred solution of the dynamic anti-interference deception navigation method for a UAV described in the present invention, the short-term navigation of the UAV includes, when the second action is triggered, switching to the inertial measurement unit navigation mode, calculating the speed of the UAV and relative displacement The attitude angle of the drone is calculated by the angular velocity of the drone to keep the attitude of the drone stable; the speed of the drone is used and relative displacement Continue to fly along the planned flight path, maintain navigation continuity in a short period of time; use the fused state vector Correct the accumulated error of the inertial measurement unit navigation mode and switch back to satellite navigation mode when the signal of the satellite navigation system is restored.

[0014] A dynamic anti-interference deception navigation system for an unmanned aerial vehicle (UAV) using any of the methods described in the present invention, wherein: a signal processing module obtains the signal of a satellite navigation system received by the UAV, identifies and filters the deception signal based on time propagation error, power estimation and pseudo-range consistency check; an adaptive filtering module performs Kalman filtering on the signal, adjusts the filter parameters in real time according to the dynamic characteristics of the UAV, and optimizes the navigation accuracy; a data fusion module switches to an inertial measurement unit navigation mode when the signal of the satellite navigation system is severely interfered, 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.

[0015] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0016] A computer-readable storage medium stores a computer program, comprising: when the computer program is executed by a processor, the steps of implementing any one of the methods of the present invention are implemented.

[0017] Beneficial effects of the present invention: The present invention can accurately identify and filter interference and deception signals by adopting multi-dimensional signal detection and analysis such as multi-frequency multi-antenna reception, time propagation error, power estimation and pseudo-range consistency. 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 UAV to improve navigation accuracy. When the satellite navigation signal is severely interfered with, short-term navigation is performed through IMU data to ensure that the UAV maintains flight stability and navigation continuity in complex environments. The fused state vector effectively corrects the IMU cumulative error and enhances the anti-interference and anti-deception capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 An overall flow chart of a dynamic anti-interference deception navigation method for a drone provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a dynamic anti-interference deception navigation method for a drone, comprising: In S1: obtaining the signal of the satellite navigation system during the flight of the UAV, detecting and identifying potential interference signals.

[0022] In S2: the filter parameters are adjusted in real time according to the motion state of the UAV to filter out noise and jump signals.

[0023] In S3: When the signal of the satellite navigation system is severely interfered, 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.

[0024] Among them, the signal of the satellite navigation system can be the signal of multiple satellite navigation systems (such as GNSS, Beidou), and the signal is received using a dual antenna or multi-antenna receiving module. The antennas are installed at different positions of the drone to provide suppression of signal multipath effects and achieve effective detection of interference signals.

[0025] It should be noted that the multipath effect refers to the delay and deviation of the signal path caused by the navigation signal being reflected by buildings, mountains, etc. before reaching the receiving antenna.

[0026] Furthermore, by analyzing the power strength of the received navigation signal and its arrival time, interference signals and spoofing signals are identified: based on the difference between the actual reception time and the theoretical arrival time of the navigation signal, the time propagation error is calculated.

[0027] Specifically, the actual reception time of each navigation satellite signal is recorded and compared with the theoretical propagation time. The theoretical propagation time can be calculated based on 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 based on the speed of light and the distance between the navigation satellite and the UAV). If the error exceeds a predetermined threshold, it indicates the existence of an abnormal propagation path, which may be caused by multipath effects or malicious interference.

[0028] 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. According to the classic 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 drone, power anomalies can be detected.

[0029] Specifically, the strength of the navigation signal will attenuate as the distance between the satellite and the drone increases. This attenuation follows the free space propagation model: , in, is the signal power received by the drone; is the signal power transmitted by the satellite; is the wavelength of the signal; is the distance from the satellite to the drone.

[0030] The system obtains the distance between the navigation satellite and the drone in real time , based on the pseudo-range, apply the free space propagation model to calculate the theoretical signal power that the drone should receive at this distance , through the receiving module of the drone, measure the actual signal power received , compare the measured actual power value with the theoretical power value calculated by the model: , in, Indicates the power error.

[0031] The pseudoranges of multiple navigation satellites are measured simultaneously and the pseudorange differences are calculated.

[0032] Specifically, the pseudorange is an estimate of the distance between the receiver and the navigation satellite, calculated by measuring the propagation time of the signal from the satellite to the receiver: , Where ρ represents pseudorange; Indicates the time it takes for the signal to arrive at the receiver; Indicates the time when the signal was transmitted from the satellite; Represents the speed of light.

[0033] In the pseudorange consistency test, the system will receive signals from multiple navigation satellites at the same time, and calculate the pseudorange value from each satellite to the drone. By comparing the pseudorange data of multiple satellites, the pseudorange difference is calculated: , in, Indicates satellite and satellite The pseudorange difference of Indicates satellite The pseudorange of Indicates satellite The pseudorange of .

[0034] Furthermore, after detecting and identifying potential interference signals, the method also includes setting weights of time propagation error, power error and pseudorange difference according to the flight mission of the UAV, obtaining a first result by weighted synthesis, and performing evaluation based on the first result.

[0035] Specifically, the first result is calculated by a weighted comprehensive evaluation method, and weights are set for the time propagation error, power error, and pseudorange difference. The size of the weight can be dynamically adjusted according to the environment, flight mission, 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 a city, the power error and time propagation error are greatly affected by buildings, so a higher weight can be given.

[0036] In actual flight, different error types have different impacts on the navigation system. Time propagation error is more prominent in scenarios with more serious multipath effects, while pseudorange consistency detection may be more important in highly open environments. Weighted comprehensive evaluation can reasonably allocate error weights according to these different situations and improve the accuracy of overall judgment.

[0037] The preset first interval, second interval and third interval are compared with the first result to determine the triggering of the action.

[0038] The actions include no action, a first action, and a second action; The trigger includes: When the first result belongs to the first interval, it is judged as no interference and no action is performed; 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 determined to be a serious interference, and the second action is performed.

[0039] Among them, the first interval, the second interval and the third interval are continuous score ranges, and according to the score size, they are the first interval, the second interval and the third interval from small to large respectively, and the first result is evaluated and assigned a value for comparison with the three intervals.

[0040] When the first result belongs to the first interval, it is judged that there is no interference and no action is performed. At this time, there may be slight interference, such as a small range of power fluctuations or time propagation errors, but this situation usually does not affect the navigation accuracy, and no excessive intervention is required, and the status quo can be maintained.

[0041] When the first result belongs to the second interval, it is judged as slight interference and the first action is performed. When the comprehensive error belongs to this interval, it means that there is a certain degree of interference or deception signal in the environment. At this time, the first action can be performed, that is, to start a more powerful anti-interference and anti-deception strategy. For example: increase the dynamic response speed of the filter, enhance the response capability to time propagation error and pseudorange anomaly; enable the fusion navigation mode of IMU (inertial measurement unit) and GNSS, so that IMU data can provide navigation support in a short time to deal with possible interference or deception signals.

[0042] When the first result belongs to the third interval, it is judged as severe interference and the second action is performed. When the comprehensive error reaches the third interval, it indicates that there is severe interference or deception signal. At this time, the second action can be performed, such as: switching to IMU navigation mode, limiting the automatic navigation function of the drone, and stopping relying on GNSS signals, performing short-term autonomous navigation through IMU or other sensors until the interference source disappears or the drone returns to a safe area; sending out a warning signal to notify the ground control center that the drone may encounter severe deception signals, and recommending manual intervention or avoidance operations.

[0043] Furthermore, the adjustment of the filter parameters includes obtaining speed and acceleration information in real time through the inertial measurement unit of the drone, 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.

[0044] By monitoring the quality indicators of the navigation signal, the measurement noise covariance matrix is ​​dynamically adjusted. When the drone encounters different signal interferences (such as multipath effects or occlusions) during flight, the measurement noise will fluctuate accordingly. The adaptive Kalman filter can dynamically adjust the RRR by monitoring the quality indicators of the navigation signal (such as signal-to-noise ratio, signal strength, etc.), thereby enhancing the signal processing capability of the filter in an interference environment.

[0045] 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. When the pseudo-range data or velocity data in the navigation signal changes abnormally in a short period of time, the filter can adjust the process noise covariance matrix to enhance the processing ability of signal jumps, quickly filter out abnormal data points, and prevent them from affecting the navigation system.

[0046] Furthermore, the data fusion includes that the inertial measurement unit obtains 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. When the second action is triggered, the signal of the satellite navigation system and the inertial measurement unit data are fused through a Kalman filter, specifically including sub-steps C1-C4: In C1: Set the state vector Contains the position, speed and attitude information of the drone: , in, is the three-dimensional coordinate of the drone; is the three-dimensional velocity; is the attitude angle (pitch, roll and yaw) of the drone.

[0047] In C2: Use the three-axis acceleration and angular velocity output by the inertial measurement unit as input to calculate the speed of the drone and relative displacement , since the IMU data is acceleration and angular velocity, the speed and position of the drone can be obtained by integrating these data, expressed as: , , in, Indicates time The speed of the moment; Indicates time The location at the moment; Indicates the sampling time interval; Indicates acceleration.

[0048] In C3: Use the acceleration and angular velocity information of the IMU to predict the state of the drone at the next moment: , in, Indicates the state of the drone at the next moment; represents the state transfer matrix; represents the input matrix; Indicates the acceleration and angular velocity output by the IMU.

[0049] In C4: Continue to detect the signal of the satellite navigation system. If there is a signal of the satellite navigation system belonging to the first interval, use the signal of the satellite navigation system to provide absolute position information and speed information, perform state correction, and calculate the fused state vector : , in, is the Kalman gain; is the GNSS measurement value; is the measurement matrix; is the GNSS measurement noise covariance.

[0050] It should be noted that GNSS signals provide absolute position information and speed information with long-term high accuracy, but may be unstable or unavailable when interfered with. Therefore, the update formula is only used for error correction when the signal of the satellite navigation system belonging to the first interval is detected. IMU navigation is mainly used the rest of the time.

[0051] Furthermore, the short-term navigation of the drone includes, when the second action is triggered, switching to the inertial measurement unit navigation mode, calculating the speed of the drone and relative displacement The attitude angle of the drone is calculated by the angular velocity of the drone to keep the attitude of the drone stable.

[0052] Harnessing the speed of drones and relative displacement Continue flying along the planned flight path, maintaining navigation continuity for a short period of time.

[0053] Use the fused state vector Correct the accumulated error of the inertial measurement unit navigation mode and switch back to satellite navigation mode when the signal of the satellite navigation system is restored.

[0054] If the above functions are implemented in the form of software functional 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0055] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0056] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0057] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0058] Embodiment 2. In an exemplary embodiment, a dynamic anti-interference deception navigation system for a UAV is also provided, including a signal processing module, which obtains the signal of the satellite navigation system received by the UAV, identifies and filters the deception signal based on time propagation error, power estimation and pseudo-range consistency check; an adaptive filtering module, which performs Kalman filtering on the signal, adjusts the filter parameters in real time according to the dynamic characteristics of the UAV, and optimizes the navigation accuracy; a data fusion module, which switches to the inertial measurement unit navigation mode when the signal of the satellite navigation system is severely interfered, 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.

[0059] Among them, the signal processing module also includes a dual-antenna or multi-antenna receiving module. By arranging antennas at different positions, the angle of signal arrival can be measured, which helps to determine the true location of the signal source and effectively filter out false signals.

[0060] Example 3, the following is an embodiment of the present invention, which provides a dynamic anti-interference deception navigation method for an unmanned aerial vehicle.

[0061] In this embodiment, a UAV equipped with a dual-antenna receiving module and an inertial measurement unit (IMU) is used to test the anti-interference and deception of the navigation system. The UAV flies in multiple flight environments (open field and urban environment), receives signals from satellite navigation systems such as GNSS and Beidou, and detects and filters interference signals, adjusts filter parameters, and performs data fusion and short-term navigation operations in the event of severe interference by implementing the technical solution of the present invention.

[0062] The beneficial effects of the present invention are verified through different flight environments. During the flight, the system records the navigation signal of each satellite and uses a tunnel to simulate the situation where the navigation signal is severely interfered with. The experimental data are shown in Table 1.

[0063] Table 1 Experimental data table

[0064] , The above experimental data show the performance of the present invention in different flight environments. In environment 1, the time propagation error and power error are kept within a small range (3.2ns and 2dB), indicating that in this environment, the navigation signal is subject to little interference and the pseudorange difference is almost non-existent (0.5m). In this scenario, the anti-interference strategy of the present invention can effectively maintain the accuracy of the signal without the need for complex anti-interference processing, and the system shows high stability.

[0065] In Environment 2 (city) and Environment 3 (tunnel), the navigation signal was significantly affected by multipath effects and interference sources. The time propagation error in Environment 2 reached 15.8ns, the power error also increased significantly (7dB), and the pseudorange difference reached 5.2 meters. Based on these data, the system determined the existence of severe interference and triggered the first action, which was to successfully maintain navigation continuity in a short period of time through IMU and GNSS fusion navigation and avoid the loss of navigation signals. In the tunnel scenario, the interference was more serious, and the pseudorange difference reached 7.8 meters. The system switched to the IMU-dominated navigation mode and made corrections after the GNSS signal was restored to ensure that the drone could successfully complete the flight mission.

[0066] In addition, in the flight data of Environment 5 and Environment 6, the system detected moderate interference, especially the pseudorange difference reached 3.6 meters and 2.1 meters respectively, but did not exceed the system's interference judgment threshold. Therefore, the system executed the first action, increasing the dynamic response speed of the filter to ensure that the slight interference in these environments can be fully responded to.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in 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 signal: 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 severely 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.

2. The dynamic anti-interference deception navigation method for unmanned aerial vehicles according to claim 1, characterized in that: 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 trigger includes: When the first result belongs to the first interval, it is judged as no interference and no action is performed; 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 determined to be a serious interference, and the second action is performed.

3. The dynamic anti-interference deception navigation method for unmanned aerial vehicles according to claim 2, characterized in that: 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 pseudorange and velocity data in the navigation signal, the process noise covariance matrix is ​​adjusted to deal with signal jumps and noise interference.

4. The dynamic anti-interference deception navigation method for unmanned aerial vehicles according to claim 3, 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 second 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 Contains the position, speed 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 speed of the drone. and relative displacement ; Use the acceleration and angular velocity information of the IMU to predict the state of the drone at the next moment; Continuously detect the signal of the satellite navigation system. If there is a signal of the satellite navigation system belonging to the first interval, use the signal of the satellite navigation system to provide absolute position information and speed information, perform state correction, and calculate the fused state vector .

5. The dynamic anti-interference deception navigation method for unmanned aerial vehicles according to claim 4, 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 speed of the UAV and relative displacement And calculate the attitude angle of the drone through the angular velocity of the drone to keep the attitude of the drone stable; Harnessing the speed of drones and relative displacement Continue to fly along the planned flight path and maintain navigation continuity for a short period of time; Use the fused state vector Correct the accumulated error of the inertial measurement unit navigation mode and switch back to satellite navigation mode when the signal of the satellite navigation system is restored.

6. A dynamic anti-interference deception navigation system for a drone using any of the methods of claims 1-5, 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.

7. 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 1-5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the dynamic anti-interference deception navigation method for a drone as described in any one of claims 1 to 5 are implemented.

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