Abstract:This paper considers a distributed filtering problem over a multi-sensor network in which the correlation of local estimation errors is unknown. Recently, this problem was studied by G. Battistelli [1] by developing a data fusion rule to calculate the weighted Kullback-Leibler average of local estimates with consensus algorithms for distributed averaging, where the weighted Kullback-Leibler average is defined as an averaged probability density function to minimize the sum of weighted Kullback-Leibler divergenc… Show more
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