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Exploiting signal nongaussianity and nonlinearity for performance assessment of adaptive filtering algorithms: Qualitative performance of Kalman filter

Mo, C, Gautama, T, Obradovic, D, Chambers, J and Mandic, D (2006) Exploiting signal nongaussianity and nonlinearity for performance assessment of adaptive filtering algorithms: Qualitative performance of Kalman filter NSSPW - Nonlinear Statistical Signal Processing Workshop 2006.

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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 nonlinear 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. © 2006 IEEE.

Item Type: Article
Authors :
NameEmailORCID
Mo, CUNSPECIFIEDUNSPECIFIED
Gautama, TUNSPECIFIEDUNSPECIFIED
Obradovic, DUNSPECIFIEDUNSPECIFIED
Chambers, Jj.a.chambers@surrey.ac.ukUNSPECIFIED
Mandic, DUNSPECIFIEDUNSPECIFIED
Date : 1 December 2006
Identification Number : 10.1109/NSSPW.2006.4378837
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:26
Last Modified : 17 May 2017 13:26
URI: http://epubs.surrey.ac.uk/id/eprint/839226

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