Risk sensitive filtering with continuous time observations

Abstract

In this article we consider risk sensitive filtering for a Markov signal which is observerd through a Brownian motion, and a Poisson process. Risk sensitive filters for the robust estimation of the signal process 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. Show more