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Receding Horizon Filtering for Mobile Robot Systems with Cross-Correlated Sensor Noises

Authors: Il Young Song, Du Yong Kim, Vladimir Shin


This paper reports on a receding horizon filtering for mobile robot systems with cross-correlated sensor noises and uncertainties. Also, the effect of uncertain parameters in the state of the tracking error model performance is considered. A distributed fusion receding horizon filter is proposed. The distributed fusion filtering algorithm represents the optimal linear combination of the local filters under the minimum mean square error criterion. The derivation of the error cross-covariances between the local receding horizon filters is the key of this paper. Simulation results of the tracking mobile robot-s motion demonstrate high accuracy and computational efficiency of the distributed fusion receding horizon filter.

Keywords: Distributed Fusion, Kalman Filter, wheeled mobile robot, fusion formula, multisensor, receding horizon

Digital Object Identifier (DOI):

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