Risk Sensitive Filtering with Poisson Process Observations
Date
2000
Authors
Malcolm, W Paul
James, Matthew
Elliott, Robert J
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Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
In this paper we consider risk sensitive filtering for Poisson process observations. Risk sensitive filtering is a type of robust filtering which offers performance benefits in the presence of uncertainties. We derive a risk sensitive filter for a stochastic system where the signal variable had dynamics described by a diffusion equation and determines the rate function for an observation process. The filtering equations are stochastic integral equations. Computer simulations are presented to demonstrate the performance gain for the risk sensitive filter compared with the risk neutral filter.
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Keywords
Keywords: Computer simulation; Integral equations; Partial differential equations; Poisson distribution; Signal filtering and prediction; Theorem proving; Change of measure; Martingale calculus; Point processes; Poisson process; Risk sensitive filtering; Markov pro
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Source
Applied Mathematics and Optimization
Type
Journal article
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Restricted until
2037-12-31