Distributed Frobenius-Norm Finite Memory Interacting Multiple Model Estimation for Mobile Robot Localization

【Author】 Suh, Duckhyun; Lee, Dong Kyu; Pak, Jung Min; Ahn, Choon Ki

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】This paper proposes a novel state estimation algorithm, called the distributed Frobenius-norm finite memory interacting multiple model (DFFM-IMM) estimation algorithm, for mobile robot localization in wireless sensor networks (WSNs). The proposed algorithm involves finite memory estimation based on recent finite measurements; such estimation facilitates robust localization in cases of missing measurements and robot kidnapping. Furthermore, the proposed algorithm employs IMM, which facilitates accurate localization if a mobile robot abruptly changes its speed and course. Notably, average-consensus-based distributed processing renders the proposed DFFM-IMM algorithm computationally efficient, and hence, real-time processing for very short sampling times of the WSN is possible. The proposed algorithm's performance is demonstrated by comparing it with a centralized Frobenius-norm finite memory IMM (CFFM-IMM) estimation algorithm and a localization algorithm on the basis of simulations and experiments.

【Keywords】Location awareness; Wireless sensor networks; Distributed processing; Estimation; Consensus algorithm; Robot sensing systems; Real-time systems; Distributed localization; finite memory estimation; interacting multiple-model; mobile robot; wireless sensor networks

【发表时间】2022

【收录时间】2023-03-12

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