This paper presents a distributed receding horizon filtering algorithm for multisensor
continuous-time linear stochastic systems. Distributed fusion with a weighted sum structure
is applied to local receding horizon Kalman filters having different horizon lengths. The
fusion estimate of the state of a dynamic system represents the optimal linear fusion by
weighting matrices under the minimum mean square error criterion. The key contribution of
this paper lies in the derivation of the differential equations for determining the error cross …

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 …