Abstract
AbstractWe study the distributed problem for state estimation of dynamic systems, in which the system and measurement noises of each subsystem are cross‐correlated, and the local state of each subsystem is also coupled with that of the neighboring subsystems. By newly constructing measurements, the noises are uncorrelated, and we derive an algorithm only using the measurements and low‐dimensional information from neighboring subsystems. Furthermore, the obtained error covariance matrices have lower and upper bounds and the proposed algorithm enjoys local stability properties. In addition, two examples are shown the effectiveness of our algorithm when compared to the centralized Kalman filter, which is the optimal algorithm for entire information.
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