Title :
Alternative Formulation of Risk-Sensitive Particle Filter (Posterior)
Author :
Bhaumik, S. ; Sadhu, S. ; Ghoshal, T.K.
Author_Institution :
Dept. of Electr. Eng., Jadavpur Univ., Kolkata
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
Keywords :
Gaussian processes; mean square error methods; particle filtering (numerical methods); Gaussian linear measurement; RMSE; linearized version; nonlinear nonGaussian system; posterior risk-sensitive particle filter; root mean square error; Adaptive filters; Additive noise; Computational efficiency; Grid computing; Nonlinear systems; Particle filters; Particle measurements; Proposals; Recursive estimation; State estimation; Particle filter; Risk-Sensitive filter; Risk-sensitive Kalman filter;
Conference_Titel :
India Conference, 2006 Annual IEEE
Conference_Location :
New Delhi
Print_ISBN :
1-4244-0369-3
Electronic_ISBN :
1-4244-0370-7
DOI :
10.1109/INDCON.2006.302801
