Abstract
An optimal filtering estimation for single sensor was extended to centralized optimal algorithm for multi-sensor system by constructing a global observation model for the multi-sensor system with multiplicative noises. Based on this algorithm, a decentralized optimal filtering fusion estimation was derived on the condition that the multiplicative noise is in the form of a general stochastic matrix and the measurement noises of each sensor are correlated with the dynamic noise, and then a decentralized global fixed-interval deconvolution algorithm was obtained. This algorithm is optimal in the sense of linear minimum-variance and the simulation results show the validity of the proposed algorithm.
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