ARAIM Protection Level Optimization Based on Feedback-Structure Subset Grouping

As an advanced algorithm for receiver autonomous integrity monitoring (RAIM), advanced RAIM (ARAIM) has gained considerable attention in the civil aviation sector and is gradually finding applications in other fields. However, with the increasing number of visible satellites, the number of fault subsets processed by the multiple hypothesis solution separation (MHSS) method grows exponentially, imposing a substantial computational burden on the receiver. Furthermore, ARAIM’s uniform distribution of integrity and continuity risks among fault subsets results in overly conservative protection levels (PLs). These challenges are often addressed as separate issues. However, this study proposes a novel PL optimization algorithm that incorporates a subset grouping method with a feedback structure to reduce the number of fault subsets, thereby decreasing detection time. In addition, an improved cuckoo search algorithm (ICSA) is developed to allocate integrity and continuity risks more effectively, optimizing the PLs. Experimental results demonstrate the effectiveness of the proposed method. Compared to ARAIM, without IMU, the protection level optimization of proposed algorithm improves by 34.38% and 35.06% in the vertical and horizontal directions, respectively; with IMU, the protection level optimization of proposed algorithm improves by 74.21% and 74.49% in the vertical and horizontal directions, respectively. In addition, due to the fault subsets reduction, the fault detection time is reduced by 54%, 47%, and 26% compared with ARAIM, FSPA, and Feedback ARAIM, respectively.