Optimal estimation via nonanticipative rate distortion function and applications to time-varying Gauss-Markov processes

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2018-01-01

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en

Pages

35

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SIAM Journal on Control and Optimization, Volume 56, issue 5, pp. 3731-3765

Abstract

In this paper, we develop finite-time horizon causal filters for general processes taking values in Polish spaces using the nonanticipative rate distortion function (NRDF). Subsequently, we apply the NRDF to design optimal filters for time-varying vector-valued Gauss-Markov processes, subject to a mean-squared error (MSE) distortion. Unlike the classical Kalman filter design, the developed filters based on the NRDF are characterized parametrically by a dynamic reverse-waterfilling optimization problem obtained via Karush-Kuhn-Tucker conditions. We develop algorithms that provide, in general, tight upper bounds to the optimal solution to the dynamic reverse-waterfilling optimization problem subject to a total and per-letter MSE distortion constraint. Under certain conditions, these algorithms produce the optimal solutions. Further, we establish a universal lower bound on the total and per-letter MSE of any estimator of a Gaussian random process. Our theoretical framework is demonstrated via simple examples.

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Keywords

Causal filters, Dynamic reverse-waterfilling, Mean-squared error distortion, Nonanticipative rate distortion function, Universal lower bound

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Citation

Stavrou, P A, Charalambous, T, Charalambous, C D & Loyka, S 2018, ' Optimal estimation via nonanticipative rate distortion function and applications to time-varying Gauss-Markov processes ', SIAM Journal on Control and Optimization, vol. 56, no. 5, pp. 3731-3765 . https://doi.org/10.1137/17M1116349