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Optimal state estimation for stochastic systems: an information theoretic approach
1997
IEEE Transactions on Automatic Control
In this paper, we examine the problem of optimal state estimation or filtering in stochastic systems using an approach based on information theoretic measures. In this setting, the traditional minimum mean-square measure is compared with information theoretic measures, Kalman filtering theory is reexamined, and some new interpretations are offered. We show that for a linear Gaussian system, the Kalman filter is the optimal filter not only for the mean-square error measure, but for several
doi:10.1109/9.587329
fatcat:5opf47dg5nb3tcidjadsg23vee