An INS/GNSS Localization Method Based on Set-Membership Filter for Unmanned Aerial Vehicles
Xujie Qin, Jun Lai, Yirui Cong, Xiangke Wang · 2024
This paper studies a set-membership filter-based inertial integrated localization problem, where the inertial navigation system (INS) error and Global Navigation Satellite System (GNSS) measurement error are unknown-but-bounded (UBB) noises. To solve this problem, we establish a strapdown INS error model and a GNSS measurement model with UBB noises. Based on the optimal set-membership filtering framework, we propose a constrained zonotopic fast resurisive localization method. Compared with the existing probabilistic integrated navigation methods, the proposed method does not need to know the specific noise distribution and has better versatility and robustness. Finally, the numerical simulation results of the INS/GNSS navigation corroborate the effectiveness of this method.