Distributed fusion filtering for discrete-time stochastic linear systems with unknown inputs

Abstract

An unbiased state filter in linear minimum variance sense is developed for discrete-time stochastic linear systems with unknown inputs and correlated noises, where there is not any prior information for the unknown inputs. When there are multiple sensors, the cross-covariance matrix of filtering errors between any two sensors is derived. Further, the distributed scalar-weighted fusion state filter is given for every state component based on the multi-sensor optimal component scalar-weighted fusion algorithm in linear minimum variance sense. Simulation example shows the effectiveness of algorithms.