Performance Issues in Non-Gaussian Filtering Problems

Abstract

Performance for filtering problems is usually measured using the second-order moment. For non-Gaussian applications, this measure is not always sufficient. In this paper, the Kull-back divergence is extensively used to compare estimated distributions. Several estimation techniques are compared, and methods with ability to express non-Gaussian posterior distributions are shown to give superior performance over classical second-order moment based estimators.