Particle Methods for Risk Sensitive Filtering

Abstract

Risk sensitive filters (RSF) are known to be robust in the presence of uncertainties in the system parameters. Unfortunately these filters only admit closed form expressions for a very limited class of models including finite state-space Markov chains and linear Gaussian models. In this paper, we present an efficient Monte Carlo particle implementation of these filters for non-linear and non-Gaussian state-space models. This non-standard particle algorithm is based on a probabilistic interpretation of the RSF recursion. This algorithm significantly extends the range of applications of risk-sensitive techniques. Simulation results demonstrate the performance of the algorithm.