Addressing Data Incest for Cooperative Localization in Multi-sensor Multi-vehicle Systems
Xiaoyu Shan · theses.fr (ABES) · 2024
Data incest problem causes inter-estimate correlation during data fusion process, which yields inconsistent data fusion result. Especially in the multi-sensor multi-vehicle (MSMV) cooperative localization system, the data incest problem is serious due to multiple relative position estimations, which not only leads to pessimistic estimation, but also causes additional computational overhead. In order to address the data incest problem, we propose a new data fusion method named interval split covariance intersection filter (ISCIF). The general consistency of the ISCIF is proven, serving as a supplementary proof for the split covariance intersection filter (SCIF). Moreover, a decentralized MSMV localization system including absolute and relative positioning stages is designed. In the absolute positioning stage, each vehicle uses the ISCIF algorithm to update its own position based on absolute measurements. In the relative position stage, the interval constraint propagation (ICP) method is implemented to preprocess multiple relative position estimates and prepare input data for ISCIF at first. Then, the proposed ISCIF algorithm is employed to realize relative positioning. Furthermore, in order to enhance the robustness of the proposed localization method in MSMV systems, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. In addition, comparative simulations demonstrate that the proposed method can achieve accurate, robust and low-cost results compared with the state of the art methods.