Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise

Robust nonlinear filters are robust against outliers in applications in which the underlying processes are non-Gaussian and impulsive. Among the classes of robust nonlinear filters, the sample myriad which is a maximum likelihood estimator of location derived for symmetric α-stable (SαS) distributio...

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Main Author:	Goh, Benny Ming Kai
Format:	Thesis
Published:	2019
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
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id	my-mmu-ep.7764
record_format	uketd_dc
spelling	my-mmu-ep.77642020-09-22T17:52:56Z Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise 2019-08 Goh, Benny Ming Kai TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Robust nonlinear filters are robust against outliers in applications in which the underlying processes are non-Gaussian and impulsive. Among the classes of robust nonlinear filters, the sample myriad which is a maximum likelihood estimator of location derived for symmetric α-stable (SαS) distribution, has gained popularity in recent years due to availability of a tuneable parameter that controls the robustness of the filter. However, the high computational cost incurred for implementing sample myriad and its related frameworks renders it impractical for certain applications such as wireless communications which require very efficient algorithms. This motivates the development of new algorithms and techniques that improves the computational efficiency of the myriad-based robust nonlinear filters. First, the sample myriad and the weighted myriad filters are commonly computed using the batch processing fixed-point algorithm. Since a block of input samples has to be gathered first before the algorithm can perform estimation, significant delay may arise if the block size is large. To address this issue, a sequential sample myriad filter and a sequential weighted myriad filter are derived to compute the estimate in real-time by updating the current estimate whenever a new input sample becomes available. The result showed that the proposed sequential techniques which have a lower computational complexity, achieve almost the same convergence speed and accuracy as the classical batch processing algorithm. 2019-08 Thesis http://shdl.mmu.edu.my/7764/ http://library.mmu.edu.my/library2/diglib/mmuetd/ phd doctoral Multimedia University Faculty of Engineering & Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Goh, Benny Ming Kai Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise
description	Robust nonlinear filters are robust against outliers in applications in which the underlying processes are non-Gaussian and impulsive. Among the classes of robust nonlinear filters, the sample myriad which is a maximum likelihood estimator of location derived for symmetric α-stable (SαS) distribution, has gained popularity in recent years due to availability of a tuneable parameter that controls the robustness of the filter. However, the high computational cost incurred for implementing sample myriad and its related frameworks renders it impractical for certain applications such as wireless communications which require very efficient algorithms. This motivates the development of new algorithms and techniques that improves the computational efficiency of the myriad-based robust nonlinear filters. First, the sample myriad and the weighted myriad filters are commonly computed using the batch processing fixed-point algorithm. Since a block of input samples has to be gathered first before the algorithm can perform estimation, significant delay may arise if the block size is large. To address this issue, a sequential sample myriad filter and a sequential weighted myriad filter are derived to compute the estimate in real-time by updating the current estimate whenever a new input sample becomes available. The result showed that the proposed sequential techniques which have a lower computational complexity, achieve almost the same convergence speed and accuracy as the classical batch processing algorithm.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Goh, Benny Ming Kai
author_facet	Goh, Benny Ming Kai
author_sort	Goh, Benny Ming Kai
title	Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise
title_short	Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise
title_full	Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise
title_fullStr	Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise
title_full_unstemmed	Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise
title_sort	robust sequential algorithms for nonlinear signal processing under impulse noise
granting_institution	Multimedia University
granting_department	Faculty of Engineering & Technology
publishDate	2019
_version_	1747829676993150976

Robust Sequential Algorithms For Nonlinear Signal Processing Under Impulse Noise

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