Unmanned aerial vehicle trusted navigation method based on anomaly detection and multi-source fusion under sensor attack

Abstract

The application discloses a method for abnormality detection and multi-source fusion of unmanned aerial vehicle (UAV) reliable navigation under sensor attack, and aims at solving the problem of navigation precision decline of the UAV navigation system in a complex environment caused by sensor attack and abnormal interference, and specifically comprises the following steps: for the problem of UAV reliable navigation, a UAV kinematics and dynamics model in Euler angle description mode is established; UAV flight states and a sensor measurement model are analyzed, and a UAV state estimation and measurement equation is established; a sensor attack and abnormal interference mathematical model is established by analyzing the sensor attack and abnormal interference encountered in the UAV navigation process; a UAV sensor attack and interference detection framework is designed according to the requirement, and a multi-source fusion strategy is implemented based on the model prediction. The application effectively improves the anti-interference capability of the UAV in a complex environment, ensures the safety, reliability and continuity of the navigation system of the UAV, and is suitable for the design, optimization and actual application scene of the UAV navigation system. The application belongs to the field of reliable multi-source navigation of aircraft.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of aircraft trusted multi-source navigation, and particularly relates to a UAV trusted navigation method for abnormality detection and multi-source fusion under sensor attacks. BACKGROUND

[0002] In recent years, with the wide application of UAV technology, the trustworthiness and anti-interference ability of UAV navigation systems have become a key research direction. Especially in complex environments, the robustness and adaptability of traditional navigation methods are insufficient due to the threat of sensor attacks (such as signal deception, replay attacks, electromagnetic interference, etc.) to the navigation system. Under these attacks, sensors may provide incorrect data, affecting the stability and accuracy of the navigation system, and even leading to navigation failure or task failure of the UAV.

[0003] In existing research, multi-source sensor data fusion technology has been proven to effectively enhance the navigation ability of UAVs. By combining multiple data sources (such as GPS, inertial measurement units, magnetometers, etc.), multi-source fusion technology can take advantage of each sensor to reduce the impact of a single sensor failure on the navigation system. However, most current navigation methods rely on simple trustworthiness determination mechanisms when facing sensor data anomalies, and cannot timely identify and isolate attacked data in dynamic environments. In addition, existing methods for solving multi-sensor anomaly detection problems mostly rely on data-driven models, which often require a large number of attack samples for training, and this has limited effectiveness for unknown types of attacks. SUMMARY

[0004] To solve the problem of navigation accuracy decline of UAV navigation systems in complex environments due to sensor attacks and abnormal interference, the application provides a UAV trusted navigation method for abnormality detection and multi-source fusion under sensor attacks.

[0005] The technical solution adopted by the application to solve the above problems is:

[0006] The UAV trusted navigation method for abnormality detection, isolation, and multi-source fusion under sensor attacks and interference provided by the application includes the following steps:

[0007] Step 1: For the problem of UAV trusted navigation, establish the kinematics and dynamics models of the UAV in the Euler angle description mode;

[0008] Step 2: Analyze the flight state of the UAV and the sensor measurement model, and establish the state estimation and measurement equations of the UAV;

[0009] Step 3: Analyze the sensor attacks and abnormal interference encountered during UAV navigation, and establish the mathematical models of attacks and interference;

[0010] Step 4: Designing a UAV sensor attack and jamming detection framework according to the requirements, and implementing a multi-source fusion strategy based on model prediction.

[0011] Further, the UAV kinematics and dynamics model described in step 1 includes the following:

[0012]

[0013] where Fx, Fy, Fz are the resultant forces generated by the propulsion and aerodynamic forces; g is the acceleration of gravity; u, v, w are the three-axis velocity vectors in the body coordinate system; are the three-axis acceleration vectors in the body coordinate system; p, q, r are the three-axis angular velocities in the body coordinate system.

[0014] The kinematic equations are as follows:

[0015]

[0016] where φ, θ, ψ are the Euler angles, is the rate of change of attitude angle in the North-East ground coordinate system.

[0017] Further, the sensor measurement model described in step 2 includes the following:

[0018]

[0019] where, and are position measurements and linear velocity measurements, respectively. P and e V represent Gaussian white noise vectors with covariance matrices Q P and Q V , respectively.

[0020] The UAV heading angle is obtained by the magnetometer:

[0021]

[0022] where e ψ represents a Gaussian white noise vector with covariance matrix Q ψ .

[0023] Further, in step 3, the UAV may encounter abnormal situations such as sensor attacks and jamming during flight, which may affect the navigation and positioning outputs discussed earlier; To solve this problem, first determine the main types of UAV sensor attacks and jamming, and perform mathematical modeling;

[0024] GPS interruption attack makes the navigation system completely lose the positioning ability; its mathematical modeling is as follows:

[0025] R(t) = 0 (signal is completely interrupted) (5) Wherein, R(t) is the received GPS signal;

[0026] The magnetic interference attack will cause the magnetic force meter to output the wrong heading angle; its mathematical modeling is as follows:

[0027] The ideal geomagnetic signal is M = [Mx, My, Mz] T ; After adding the interference signal ΔM, the output signal is:

[0028] M' = M + ΔM (6)

[0029] Where, ΔM represents the influence of external interference, which may cause the heading angle error;

[0030] The deception attack induces the unmanned aerial vehicle navigation system to make a wrong decision by forging signals or modifying real signals,

[0031] The GPS deception attack will make the unmanned aerial vehicle calculate the wrong position or speed information; its mathematical modeling is as follows:

[0032] Suppose that the real GPS signal is S(t), and the forged signal is S'(t), then the received signal R(t) is:

[0033] R(t) = S'(t) (7)

[0034] The forged signal S'(t) can be realized by injecting false position offset ΔP and velocity offset ΔV:

[0035] P'(t) = P(t) + ΔP (8)

[0036] V'(t) = V(t) + ΔV (9)

[0037] Wherein, P(t), V(t) are the real position and speed respectively.

[0038] Further, in step 4, the abnormality detection of unmanned aerial vehicle sensor attack and interference is divided into four cases, the detection results of GPS and magnetometer are combined, different types of sensor attacks are classified and identified, and the identified results are used as the basis for multi-source fusion.

[0039] The beneficial effects of the present application are:

[0040] The application can identify potential attacks in time by introducing a dynamic anomaly detection and isolation mechanism to monitor the consistency of sensor data and multi-source navigation information in real time, and can realize full use of trusted data by dynamically adjusting data weights through multi-source fusion technology, thereby ensuring the robustness and accuracy of the unmanned aerial vehicle navigation system in complex environments.

[0041] The application designs an innovative method of integrating sensor anomaly detection and multi-source fusion to enhance the reliability and anti-interference ability of the unmanned aerial vehicle navigation system, which not only solves the safety problem in complex environments, but also provides theoretical and engineering support for realizing intelligent and trusted unmanned aerial vehicle navigation. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a flowchart of the unmanned aerial vehicle trusted navigation method of the application for sensor attack and interference anomaly detection, isolation and multi-source fusion. DETAILED DESCRIPTION

[0043] As shown in Figure 1 , the specific operation steps of the unmanned aerial vehicle trusted navigation method of the application for sensor attack anomaly detection and multi-source fusion are as follows:

[0044] S1: First, construct the linear motion dynamics equation of the unmanned aerial vehicle, as follows:

[0045]

[0046] Where F is the total external force received by the aircraft, m is the weight of the unmanned aerial vehicle, and V is the speed of the aircraft obtained in the ground coordinate system.

[0047] Next, the angular motion dynamics equation can be expressed as follows:

[0048]

[0049] Where M is the total external moment received by the aircraft (only gravity and air force), and L is the momentum moment.

[0050] The force equations of the unmanned aerial vehicle are as follows:

[0051]

[0052] Where Fx, Fy, and Fz are the total forces generated by the propulsion and aerodynamic forces in the body coordinate system; g is the acceleration of gravity; u, v, and w are the three-axis velocity vectors in the body coordinate system; are the three-axis acceleration vectors in the body coordinate system; p, q, and r are the three-axis angular velocities in the body coordinate system.

[0053] The kinematic equations are as follows:

[0054]

[0055] where φ, θ, ψ are Euler angles, is the rate of change of attitude angle in the North-East-Down ground coordinate system.

[0056] S2: analyze the flight state of the unmanned aerial vehicle and the sensor measurement model, establish the unmanned aerial vehicle state estimation and measurement equation; the sensors used in the application include GPS, IMU, magnetometer. First, the unmanned aerial vehicle obtains its position measurement P~ and linear velocity measurement

[0057]

[0058] where, and are the position measurement and linear velocity measurement respectively, e P and e V represent Gaussian white noise vectors with covariance matrices Q P and Q V respectively.

[0059] The heading angle of the unmanned aerial vehicle is obtained by the magnetometer:

[0060]

[0061] where e ψ represents a Gaussian white noise vector with covariance matrix Q ψ .

[0062] The inertial measurement unit (IMU) sensor mainly obtains the acceleration and angular velocity of the unmanned aerial vehicle through the accelerometer and gyroscope, and can further obtain the speed and position information through calculation. First, the accelerometer measurement model of the IMU is as follows:

[0063]

[0064] where, is the acceleration measurement value of the unmanned aerial vehicle, b a is the fixed offset of the IMU accelerometer, e a represents zero-mean Gaussian white noise.

[0065] Then, the measurement model of the gyroscope of the IMU is given:

[0066]

[0067] where, is the gyroscope measurement value of the unmanned aerial vehicle, To fix the bias noise of the gyroscope,

[0068] represents the zero-mean Gaussian white noise of the gyroscope.

[0069] S3: Analyze the sensor attacks and abnormal interference encountered in the navigation process of the UAV, and establish a mathematical model of the attack and interference.

[0070] The UAV may encounter abnormal situations such as sensor attacks and interference during flight, which may affect the navigation and positioning outputs discussed earlier. To solve this problem, we first identify the main types of UAV sensor attacks and interference, and conduct mathematical modeling.

[0071] Attacks on the sensor layer are divided into two categories according to the attack method: jamming attacks and deception attacks. Jamming attacks introduce external interference or interruption to the sensor signal, causing it to fail or output incorrect data. This type of attack includes the following: GPS interruption, magnetic interference.

[0072] GPS interruption attack features: GPS signals are transmitted to the UAV navigation system through satellites, which are vulnerable to electromagnetic interference or signal power suppression, or are blocked by shielding devices to prevent GPS signals from reaching the UAV, causing the navigation system to completely lose positioning ability. Its mathematical modeling is as follows:

[0073] R(t) = 0 (signal completely interrupted) (18) where R(t) is the received GPS signal.

[0074] Magnetic interference attack features: the magnetometer is sensitive to the geomagnetic field and is easily affected by strong electromagnetic interference or nearby magnetic objects. Interference can cause the magnetometer to output incorrect heading angles. Its mathematical modeling is as follows:

[0075] The ideal geomagnetic signal is M = [Mx, My, Mz] T . After adding the interference signal ΔM, the output signal is:

[0076] M' = M + ΔM (19)

[0077] where ΔM represents the influence of external interference, which can cause heading angle errors.

[0078] Deception attacks induce the UAV navigation system to make incorrect decisions by falsifying signals or modifying real signals,

[0079] GPS deception attack features: the attacker sends falsified GPS signals, causing the UAV to calculate incorrect position or speed information. Its mathematical modeling is as follows:

[0080] Assuming that the real GPS signal is S(t) and the fake signal is S'(t), the received signal R(t) is:

[0081] R(t) = S'(t) (20)

[0082] The fake signal S'(t) can be realized by injecting a false position offset ΔP and a velocity offset ΔV:

[0083] P'(t) = P(t) + ΔP (21)

[0084] V'(t) = V(t) + ΔV (22)

[0085] Where P(t) and V(t) are the real position and velocity, respectively.

[0086] S4: The anomaly detection of UAV sensor attack and interference is divided into four cases, as shown in Table 1:

[0087] Table 1 Proposed detection and fusion scheme

[0088] Serial number GPS Magnetometer Detection result 1 No alarm No alarm No anomaly 2 Alarm No alarm GPS attack 3 No alarm Alarm Magnetic interference attack 4 Alarm Alarm GPS attack and magnetic interference attack

[0089] By combining the GPS and magnetometer detection results, different types of sensor attacks can be classified and identified. The identified results are dynamically multi-source fused through the EKF algorithm to obtain a trusted UAV navigation. This method can provide a basis for the anomaly detection framework and multi-source data fusion strategy, thereby enhancing the robustness and credibility of the navigation system.

[0090] The above steps can complete the anomaly detection and multi-source fusion of the trusted UAV navigation under sensor attack.

[0091] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the present application, and any simple modification, equivalent replacement and improvement of the above embodiments within the scope of the present application, according to the technical essence of the present application, within the spirit and principles of the present application, are all within the protection scope of the present application.

Claims

1. A reliable navigation method for unmanned aerial vehicles (UAVs) based on anomaly detection, isolation, and multi-source fusion under sensor attacks and interference, characterized in that: The method includes the following steps: Step 1: To address the reliable navigation problem of UAVs, establish a kinematic and dynamic model of the UAV using Euler angles. Step 2: Analyze the UAV flight state and sensor measurement model, and establish UAV state estimation and measurement equations; Step 3: Analyze sensor attacks and abnormal interference encountered during UAV navigation, and establish mathematical models of attacks and interference; including: The mathematical model for a GPS disruption attack is as follows: R(t)=0(5) Where R(t) is the received GPS signal; The mathematical model for magnetic interference attacks is as follows: In an ideal scenario, the geomagnetic signal is M = [Mx, My, Mz]. T After adding the interference signal ΔM, the output signal is: M' = M + △M(6) Wherein, ΔM represents the influence of external disturbances, which may lead to heading angle errors; The mathematical model for GPS spoofing attacks is as follows: Assuming the real GPS signal is S(t) and the fake signal is S'(t), then the received signal R(t) is: R(t)=S'(t)(7) The spoofed signal S'(t) can be achieved by injecting false position offsets ΔP and velocity offsets ΔV: P'(t) = P(t) + ΔP(8) V'(t) = V(t) + ΔV(9) Where P(t) and V(t) are the actual position and linear velocity, respectively; Step 4: Design a framework for detecting drone sensor attacks and interference based on the requirements, and implement a multi-source fusion strategy based on model prediction. The anomaly detection of drone sensor attacks and interference is divided into four cases: no alarms from GPS and magnetometer, only GPS alarm, only magnetometer alarm, and alarms from both GPS and magnetometer. By combining the detection results of GPS and magnetometer, different types of sensor attacks are classified and identified, and the identified results are used as the basis for multi-source fusion.

2. The reliable UAV navigation method for anomaly detection, isolation, and multi-source fusion under sensor attacks and interference as described in claim 1, characterized in that, The UAV kinematics and dynamics model described in step 1 includes the following: (1) Where Fx, Fy, and Fz are the resultant forces generated by the engine power and aerodynamic forces in the body coordinate system; g is the acceleration due to gravity; and u, v, and w are the three-axis velocity vectors in the body coordinate system. p, q, r are the three-axis acceleration vectors in the body coordinate system; p, q, r are the three-axis angular velocities in the body coordinate system. The kinematic equations are as follows: (2) in, Euler angles, This represents the rate of change of attitude angle in the northeast coordinate system.

3. The reliable UAV navigation method for anomaly detection, isolation, and multi-source fusion under sensor attacks and interference as described in claim 1, characterized in that, The sensor measurement model mentioned in step 2 includes the following: (3) in, and These are position measurement and linear velocity measurement, respectively. and They represent the values ​​with covariance matrix respectively. and Gaussian white noise vector; Drone heading angle Obtained via magnetometer: (4) in, Indicates a matrix with covariance Gaussian white noise vector.

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