Risk Sensitive Estimators for Inaccurately Modelled Systems

Author

Bhaumik, Shovan ; Sadhu, Smita ; Ghoshal, Tapan Kumar

Author_Institution

Department of Electrical Engineering, Jadavpur University, Kolkata - 700 032, India. Tel & Fax: +913324146723, E-mail: shovan.bhaumik@gmail.com

fYear

2005

fDate

11-13 Dec. 2005

Firstpage

86

Lastpage

91

Abstract

Robustness of risk sensitive (RSE) estimators/filters for inaccurately modelled plant are elucidated and exemplified. A theorem which allows alternative pathway for deriving RSE filter relation and derivation of different closed form relations for RS filters in linear Gaussian cases is provided. Consequently, errors in expressions in earlier publications have been detected and rectified. Properties of RS filters are briefly reviewed and the interpretation of robustness of RS filters elaborated. Using Monte Carlo simulation, it is shown that RS filters perform significantly better compared to risk-neutral filters when (i) process noise covariance is in error (ii) the true system (truth model) contains unmodelled bias (iii) the state transition matrix is inaccurately known. Design pragmatics for the choice of the risk sensitive parameter is indicated.

Keywords

Kalman filter; Model uncertainty; Risk sensitive filter; Robust Estimation; Costs; Covariance matrix; Kernel; Noise measurement; Noise robustness; Nonlinear filters; Random variables; Robust control; State estimation; Uncertainty; Kalman filter; Model uncertainty; Risk sensitive filter; Robust Estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

INDICON, 2005 Annual IEEE

Print_ISBN

0-7803-9503-4

Type

conf

DOI

10.1109/INDCON.2005.1590130

Filename

1590130