A comparative study of the Benes filtering problem

Abstract

This paper studies a non-linear filter due to Benes for which the optimum solution is known. The paper compares the estimation performance of Benes filter to those of well-known approximate filters: the Extended Kalman, the statistical linearisation and the particle filtering. The performance of all these four filters are also compared to the Cramer–Rao lower bound. Thus, the Benes filter is a yardstick to rank the above-mentioned known techniques in terms of performance and computational cost which solve in an approximate manner the problem solved in an optimum way by Benes. This class of non-linear filtering problem has an interest in application problems like the target tracking.