Risk-sensitive filtering and smoothing for continuous-time Markov processes

W. Paul Malcolm; Robert J. Elliott; Matthew R. James

doi:10.1109/TIT.2005.846405

Risk-sensitive filtering and smoothing for continuous-time Markov processes

W. Paul Malcolm^*, Robert J. Elliott, Matthew R. James

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

We consider risk sensitive filtering and smoothing for a dynamical system whose output is a vector process in ℝ². The components of the observation process are a Markov process observed through a Brownian motion and a Markov process observed through a Poisson process. Risk-sensitive filters for the robust estimation of an indirectly observed Markov state processes are given. These filters are stochastic partial differential equations for which robust discretizations are obtained. Computer simulations are given which demonstrate the benefits of risk sensitive filtering.

Original language	English
Pages (from-to)	1731-1738
Number of pages	8
Journal	IEEE Transactions on Information Theory
Volume	51
Issue number	5
DOIs	https://doi.org/10.1109/TIT.2005.846405
Publication status	Published - May 2005

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10.1109/TIT.2005.846405

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abstract = "We consider risk sensitive filtering and smoothing for a dynamical system whose output is a vector process in ℝ2. The components of the observation process are a Markov process observed through a Brownian motion and a Markov process observed through a Poisson process. Risk-sensitive filters for the robust estimation of an indirectly observed Markov state processes are given. These filters are stochastic partial differential equations for which robust discretizations are obtained. Computer simulations are given which demonstrate the benefits of risk sensitive filtering.",

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AB - We consider risk sensitive filtering and smoothing for a dynamical system whose output is a vector process in ℝ2. The components of the observation process are a Markov process observed through a Brownian motion and a Markov process observed through a Poisson process. Risk-sensitive filters for the robust estimation of an indirectly observed Markov state processes are given. These filters are stochastic partial differential equations for which robust discretizations are obtained. Computer simulations are given which demonstrate the benefits of risk sensitive filtering.

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DO - 10.1109/TIT.2005.846405

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JF - IEEE Transactions on Information Theory

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Risk-sensitive filtering and smoothing for continuous-time Markov processes

Abstract

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