Tracking Dynamic Systems in α-Stable Environments

Taleb, Pouria; Werner, Stefan; Li, Shengxi; Mandic, D. P.

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Tracking Dynamic Systems in α-Stable Environments

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Title:	Tracking Dynamic Systems in α-Stable Environments
Author(s):	Taleb, Pouria ; Werner, Stefan ; Li, Shengxi ; Mandic, D. P.
Date:	2019-05-01
Language:	en
Pages:	5 4853-4857
Department:	Risto Wichman Group Norwegian University of Technology Imperial College London
ISBN:	978-1-4799-8132-8 978-1-4799-8131-1
Series:	ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN:	1520-6149 2379-190X
DOI-number:	10.1109/ICASSP.2019.8682736
Keywords:	Convergence, Random processes, Adaptation models, Calculus, Mathematical model, Measurement uncertainty, State estimation, α-stable signals, fractional-order calculus, fractional-order filtering, adaptive filtering/tracking
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Citation: Taleb , P , Werner , S , Li , S & Mandic , D P 2019 , Tracking Dynamic Systems in α-Stable Environments . in 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings . , 8682736 , Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing , IEEE , pp. 4853-4857 , IEEE International Conference on Acoustics, Speech, and Signal Processing , Brighton , United Kingdom , 12/05/2019 . https://doi.org/10.1109/ICASSP.2019.8682736
Abstract: In order to accommodate for modern adaptive filtering applications, the classic adaptive filtering paradigm is considered from a more general perspective. The new formulation allows for time dependent variations in the state of the system and more importantly it relaxes the Gaussian assumption to the generalized setting of α-stable distributions. In this work, based on the principles of gradient descent and fractional-order calculus, a cost-effective technique for tracking the state of such a system is derived. For rigour, performance of the derived filtering technique is analyzed and convergence conditions are established.

Permanent link to this item: http://urn.fi/URN:NBN:fi:aalto-201906033408 Email this Export to RefWorks QR Code Print BibTex Tweet