Distributed fusion receding horizon filtering

This paper is concerned with distributed receding horizon filtering for multisensor continuous-time linear systems. A distributed fusion with the weighted sum structure is applied to the local receding horizon Kalman filters (LRHKFs) based on the different horizon time intervals. The proposed distributed algorithm has a parallel structure and allows parallel processing of observations, thereby it more reliable than the centralized version if some sensors become faulty. In addition, the choice of receding horizon strategy makes the proposed algorithm robust against dynamic model uncertainties. The key idea of this paper lies in the derivation of the differential equations for error cross-covariances between the LRHKFs. The application of the proposed distributed filter demonstrates its effectiveness.