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.
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