Abstract
Least-squares linear estimation of signals from randomly delayed measurements acquired from multiple sensors with random delays modeled by homogeneous Markov chains is addressed. Assuming that the state-space model is unknown and using the information provided by the covariance functions of the processes involved in the observation equations, the signal estimation problem is studied by distributed and centralized methods to fuse the information provided by different sensors. Distributed and centralized filtering and fixed-point smoothing algorithms are derived using an innovation approach. The goodness of the proposed distributed and centralized filters and smoothers is compared by examining their respective error covariance matrices.
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