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

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.