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Hidden state estimation in linear systems is a popular and broad research topic which became a mainstream research area after Rudolf Kalman's seminal paper. The Kalman Filter (KF) gives the optimal solution to the estimation problem in a setting where all the processes are Gaussian random processes. However because of the suboptimal behavior of the KF in non-Gaussian settings, there is a need for a new filter that can extract higher order information from the signals. In this paper we propose using an information theoretic cost function utilizing the similarity measure Correntropy as a performance index. This results in a different perspective on hidden state estimation. We present the superior performance of the new filter on both synthetic data and on adaptive background estimation problem and discuss future research directions.
Date of Conference: July 31 2011-Aug. 5 2011