Unmanned aerial vehicle GNSS deception detection method based on flight path

Abstract

The invention discloses an unmanned aerial vehicle GNSS deception detection method based on a flight path, and mainly solves problems that an attack risk is increased due to the change of a task flight path and the interference is easily caused due to the dependence on a sensor in the prior art. According to the scheme, a guidance position is calculated by means of an expected course angle obtained by a guidance system and a flight speed and a navigation position obtained by a navigation system; a navigation track and a guidance track are obtained according to the two positions; whether the distance deviation between the navigation position and the guidance position is continuously larger than a threshold value or not is judged, whether the average value of the distance deviation between the two tracks is larger than the threshold value or not is judged, and whether the distance deviation between the two track end points is larger than the threshold value or not is judged, if so, the unmanned aerial vehicle is determined to be subjected to spoofing attack, and the unmanned aerial vehicle does not use GNSS data any more and finishes detection; otherwise,the data is continuously used to realize cyclic detection of the unmanned aerial vehicle. According to the invention, the risk that the unmanned aerial vehicle is subjected to spoofing attacks is reduced, the safety of an unmanned aerial vehicle system is enhanced, and the method can be used for detecting external attacks of the unmanned aerial vehicle system.

Description

technical field [0001] The invention belongs to the technical field of information security, and in particular relates to a track-based UAV GNSS deception detection method, which can be used to detect external attacks of UAV systems. Background technique [0002] Global navigation satellite systems GNSS, including GPS, Beidou satellite navigation system, Glonass, and Galileo, are important sources of information for UAS positioning. UAV navigation systems often use multi-sensor data fusion algorithms to fuse redundant information from multiple sensors, including data collected by sensors such as GNSS sensors, magnetic heading sensors, accelerometers, and gyroscopes, which effectively improves the accuracy of state estimation. and robustness. However, positioning systems based on multi-sensor fusion are still subject to GNSS spoofing attacks, for example, the document "Drift with Devil: Security of Multi-Sensor Fusion-based Localization in High-Level Autonomous Driving under...

AI Technical Summary

Problems solved by technology

[0004] First, because each inspection takes 4 to 8 minutes to fly, it will consume more power energy, and small UAVs have limited power energy and short range, so they are not suitable for small UAVs;

[0005] The second is that the UAV in this method must fly along the virtual detection route, causing the UAV to deviate from the mission route, may crash into surrounding obstacles, and stay in the same area for too long, increasing the risk of being attacked;

[0006] The third is that the method uses a magnetic heading sensor to measure the magnetic heading, and the magnetic heading sensor is susceptible to electromagnetic interference, for example, the interference of the strong magnetic field of the high-voltage line makes the measured magnetic heading inaccurate and reduces the feasibility of detection.

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