Abstract
The direct estimation of optimal steady-state gain in the single filtering process introduced by B. Carew et al. (1973) is extended to multicoordinated systems, and the distributed optimal steady-state gains are directly estimated for adaptive distributed filtering. The correlation method using distributed innovation processes is used. The algorithm assumes little prior information about the unknown covariances and adaptively changes the weights to best integrate the distributed estimates obtained in local filtering processes. The term best is used in the sense that the result of the adaptive distributed filtering is as close to that of the optimal distributed filtering as possible. >
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