Alternative Formulation of Risk-Sensitive Particle Filter (Posterior)

Abstract

An algorithm for posterior risk-sensitive particle filter for nonlinear non-Gaussian system has been proposed in this paper. For Gaussian linear measurement case optimal proposal and for nonlinear Gaussian measurement case linearized version of optimal proposal for risk-sensitive particle filter is derived. The applicability of nonlinear risk-sensitive filters such as extended risk-sensitive filter (ERSF), central difference risk-sensitive filter (CDRSF) as a proposal for risk-sensitive particle filter is discussed. The proposed filter is applied to a highly nonlinear Gaussian system. Results are provided to show the comparative performance of extended risk-sensitive filter (ERSF), posterior risk-sensitive particle filter (RSPF) and adaptive grid risk-sensitive filter (AGRSF) for a representative run. Root mean square error (RMSE) of the proposed filter has also been provided and compared with ERSF and AGRSF. The computational cost of the proposed risk-sensitive estimator is studied and compared with other nonlinear risk-sensitive filters