Exploiting Signal Nongaussianity and Nonlinearity for Performance Assessment of Adaptive Filtering Algorithms: Qualitative Performance of Kalman Filter

Abstract

A new framework for the assessment of the qualitative performance of Kalman filter is proposed. This is achieved by the recently proposed `Delay Vector Variance' (DVV) method for the signal modality characterisation, which is based upon the local predictability in the phase space. It is shown that Kalman filter not only outperforms common linear and non-linear filters in terms of quantitative performance but also achieves a better qualitative performance. A set of comprehensive simulations on representative data sets supports the analysis.