A robust particle filter for ambiguous updates of underwater terrain-aided navigation

Abstract

This paper proposes a novel method to improve the robustness of particle filtering (PF) in relatively flat terrain that lack sufficient features for accurate positioning. In terrain -aided navigation (TAN), similar terrain profiles lead to multimodality in the posterior distribution. It not only reduces the accuracy of current position estimation, but also affects the subsequent estimation of PF, and even causes filter divergence. When no additional information is available, the historical estimation is employed to correct ambiguous updates caused by multimodal posteriors. First, a strategy for identifying ambiguous updates is proposed by analyzing the particle set distribution using the clustering method and covariance. Inspired by out-of-sequence measurement (OOSM), once the ambiguous updates are detected, the ambiguous estimates are corrected by introducing high-quality previous information. Moreover, an efficient solution is provided for the storage and computation requirements of OOSM within the PF framework. To verify the effectiveness of the proposed algorithm, simulation and experimental validation are designed. By comparing with PF, mixture particle filtering (MPF), and OOSMPF algorithms, the proposed algorithm demonstrates better estimation accuracy and robustness in terrain flat areas.