With the development of satellite navigation technology, the research focus of GNSS has shifted from improving positioning accuracy to expanding system application and improving system performance. At the same time, improving the survivability of satellite navigation systems has become a research hotspot in the field of navigation, especially with regard to anti-spoofing. This paper first briefly analyzes the common interference types of satellite navigation and then focuses on spoofing. We analyze the characteristics and technical mechanism of satellite navigation and the positioning signal. Spoofing modes are classified and introduced separately according to signal generation, implementation stage and deployment strategy. After an introduction of GNSS spoofing technology, we summarize the research progress of GNSS anti-spoofing technology over the last decade. For anti-spoofing technology, we propose a new classification standard and analyze and compare the implementation difficulty, effect and adaptability of the current main spoofing detection technologies. Finally, we summarize with considerations, prospective challenges and development trends of GNSS spoofing and anti-spoofing technology in order to provide a reference for future research.
The small UAV (unmanned aerial vehicle) cluster has become an important trend in the development of UAVs because it has the advantages of being unmanned, having a small size and low cost, and ability to complete many collaborative tasks. Meanwhile, the problem of GPS spoofing attacks faced by submachines has become an urgent security problem for the UAV cluster. In this paper, a GPS-adaptive spoofing detection (ASD) method based on UAV cluster cooperative positioning is proposed to solve the above problem. The specific technical scheme mainly includes two detection mechanisms: the GPS spoofing signal detection (SSD) mechanism based on cluster cooperative positioning and the relative security machine optimal marking (RSOM) mechanism. The SSD mechanism starts when the cluster enters the task state, and it can detect all threats to the cluster caused by one GPS signal spoofing source in the task environment; when the function range of the mechanism is exceeded, that is, there is more than one spoofing source and more than one UAV is attacked by different spoofing sources, the RSOM mechanism is triggered. The ASD algorithm proposed in this work can detect spoofing in a variety of complex GPS spoofing threat environments and is able to ensure the cluster formation and task completion. Moreover, it has the advantages of a lightweight calculation level, strong applicability, and high real-time performance.
Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm’s insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV’s ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.
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