Tracking analysis of minimum kernel risk-sensitive loss algorithm under general non-Gaussian noise

Rastegarnia, A., Malekian, P., Khalili, A., Bazzi, W.M. and Sanei, S. ORCID: 0000-0002-3437-2801, 2018. Tracking analysis of minimum kernel risk-sensitive loss algorithm under general non-Gaussian noise. IEEE Transactions on Circuits and Systems II: Express Briefs. ISSN 1549-7747

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Abstract

In this paper the steady-state tracking performance of minimum kernel risk-sensitive loss (MKRSL) in a non-stationary environment is analyzed. In order to model a non-stationary environment, a first-order random-walk model is used to describe the variations of optimum weight vector over time. Moreover, the measurement noise is considered to have non-Gaussian distribution. The energy conservation relation is utilized to extract an approximate closed-form expression for the steady-state excess mean square error (EMSE). Our analysis shows that unlike for the stationary case, the EMSE curve is not an increasing function of step-size parameter. Hence, the optimum step-size which minimizes the EMSE is derived. We also discuss that our approach can be used to extract steady-state EMSE for a general class of adaptive filters. The simulation results with different noise distributions support the theoretical derivations.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Circuits and Systems II: Express Briefs
Creators:	Rastegarnia, A., Malekian, P., Khalili, A., Bazzi, W.M. and Sanei, S.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	30 November 2018
ISSN:	1549-7747
Identifiers:	Number Type 10.1109/tcsii.2018.2874969 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	23 Oct 2018 14:50
Last Modified:	23 Oct 2018 14:50
URI:	https://irep.ntu.ac.uk/id/eprint/34720

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