Distributed Receding Horizon Filtering for Linear Multisensor Contiuous-Time Systems

I.Y. Song, D.Y. Kim, V. Shin, G. Shevlyakov, and K. Kim (Korea)


Multisensor, Receding horizon, Kalman filter, Fusion formula, Distributed fusion


This paper is concerned with distributed receding horizon filtering for continuous-time linear systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal receding horizon filters. The distributed fusion algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The derivation of the error cross covariances between local RHKFs is the key of this paper. The application to a tracking system with two sensors demonstrates effectiveness of the distributed fusion of receding horizon filters.

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