Homotopy-safe trajectory planning and attack detection for low-altitude UAV under false map information injection attacks

Low-altitude unmanned aerial vehicles (UAVs) have been extensively deployed in logistics support, surveillance, and disaster relief. However, their open communication networks and inherently vulnerable navigation systems render them susceptible to false map information injection attacks (FMIIA). To effectively mitigate the impact of FMIIA, this paper presents a UAV local flight trajectory optimization framework that integrates a robust attack detection method based on initial excitation (IE) and an efficient local path reconstruction approach utilizing the Marden theorem. First, an IE-based adaptive robust observer is formulated, where IE enhances the observability of the system’s input-output responses, enabling joint estimation of the UAV’s true state, attack signals, and environmental disturbances. The FMIIA characteristics are precisely identified through residual analysis and state estimation error evaluation. Upon detection of an attack, trajectory reconstruction is performed utilizing the Marden theorem. The attack detection results are incorporated as constraints, and the homotopy-safe flight corridors are generated, ensuring that the UAV effectively circumvents the compromised area. Finally, the trajectory planning problem is reformulated as a constrained polynomial optimization problem, producing a continuous, safe, and dynamically feasible local flight trajectory within the designated corridors. Comprehensive validation, from numerical analysis of observer stability to real-world flight tests, demonstrates that the proposed HSTP framework achieves a 96.83% faster processing time than state-of-the-art methods, while simultaneously ensuring superior trajectory stability, precise tracking, and robust real-time replanning against dynamic FMIIA threats.