Tracking Dynamic Systems in α-Stable Environments

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

doi:10.1109/ICASSP.2019.8682736

Tracking Dynamic Systems in α-Stable Environments

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

Risto Wichman Group

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

27 Citations (Scopus)

303 Downloads (Pure)

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.

Original language	English
Title of host publication	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings
Publisher	IEEE
Pages	4853-4857
Number of pages	5
ISBN (Electronic)	978-1-4799-8131-1
ISBN (Print)	978-1-4799-8132-8
DOIs	https://doi.org/10.1109/ICASSP.2019.8682736
Publication status	Published - 1 May 2019
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44

Publication series

Name	Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP
Country/Territory	United Kingdom
City	Brighton
Period	12/05/2019 → 17/05/2019

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

Access to Document

10.1109/ICASSP.2019.8682736

ELEC_Talebi_Tracking_Dynamic_Systems_ICASSP2019Accepted author manuscript, 953 KB

Cite this

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 (pp. 4853-4857). Article 8682736 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2019.8682736

Taleb, Pouria ; Werner, Stefan ; Li, Shengxi et al. / Tracking Dynamic Systems in α-Stable Environments. 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. pp. 4853-4857 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

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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.",

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",

author = "Pouria Taleb and Stefan Werner and Shengxi Li and Mandic, \{D. P.\}",

year = "2019",

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booktitle = "44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings",

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Taleb, P, Werner, S, Li, S & Mandic, DP 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

Tracking Dynamic Systems in α-Stable Environments. / Taleb, Pouria; Werner, Stefan; Li, Shengxi et al.
44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. p. 4853-4857 8682736 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Tracking Dynamic Systems in α-Stable Environments

AU - Taleb, Pouria

AU - Werner, Stefan

AU - Li, Shengxi

AU - Mandic, D. P.

N1 - Conference code: 44

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

KW - Convergence

KW - Random processes

KW - Adaptation models

KW - Calculus

KW - Mathematical model

KW - Measurement uncertainty

KW - State estimation

KW - α-stable signals

KW - fractional-order calculus

KW - fractional-order filtering

KW - adaptive filtering/tracking

UR - https://www.scopus.com/pages/publications/85068998203

U2 - 10.1109/ICASSP.2019.8682736

DO - 10.1109/ICASSP.2019.8682736

M3 - Conference article in proceedings

SN - 978-1-4799-8132-8

T3 - Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

SP - 4853

EP - 4857

BT - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings

PB - IEEE

T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 12 May 2019 through 17 May 2019

ER -

Taleb P, Werner S, Li S, Mandic DP. 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. IEEE. 2019. p. 4853-4857. 8682736. (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). doi: 10.1109/ICASSP.2019.8682736

Tracking Dynamic Systems in α-Stable Environments

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Robust Demand-End Optimization with Event-Triggered Situational Awareness

Cite this

Tracking Dynamic Systems in α-Stable Environments

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Robust Demand-End Optimization with Event-Triggered Situational Awareness

Cite this