Abstract
This paper is concerned with state estimation of a linear system by fusion of asynchronous, multirate, multisensor data. The system is described at the finest scale with multiple sensors observing a common thing independently at different scales with different sampling rates. Unreliable measurements are available. The main results are: the multiscale system is properly formulated, a credibility measure is constructed and used to determine whether the measurement is normal or faulty, and an optimal recursive fusion algorithm is presented. The effectiveness and stability of the algorithm under unreliable measurements are analyzed. A numerical example is given to show the feasibility and effectiveness of the presented algorithm.
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