Distributed receding horizon filtering for mixed continuous-discrete multisensor linear stochastic systems

A new distributed receding horizon filtering algorithm for mixed continuous-discrete linear systems with different types of observations is proposed. The distributed fusion filter is formed by summation of the local receding horizon Kalman filters (LRHKFs) with matrix weights depending only on time instants. The proposed distributed filter has a parallel structure and allows parallel processing of measurements; thereby, it is more reliable than the centralized version if some sensors become faulty. Also, the selection of the receding horizon strategy makes the proposed distributed filter robust against dynamic model uncertainties. The key contribution of this paper is the derivation of the error cross-covariance equations between the LRHKFs in order to compute the optimal matrix weights. High accuracy and efficiency of the proposed distributed filter are demonstrated on the damper harmonic oscillator motion and the water tank mixing system.