Malcolm, W PaulJames, MatthewElliott, Robert J2015-12-130095-4616http://hdl.handle.net/1885/89950In 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.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 pro20002015-12-12