Complex-valued nonlinear adaptive filters for noncircular signals

Complex signal has been the backbone of large class of signals encountered in many modern applications as biomedical engineering, power system, radar, communication system, renewable energy and military technologies. However, statistical signal processing in complex domain are suited to only the con...

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Main Author:	Cyprian, Amadi Chukwuemena
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Electrical engineering Signal processing
Online Access:	http://psasir.upm.edu.my/id/eprint/71208/1/FK%202017%2072%20-%20IR.pdf
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spelling	my-upm-ir.712082019-08-29T08:35:17Z Complex-valued nonlinear adaptive filters for noncircular signals 2017-05 Cyprian, Amadi Chukwuemena Complex signal has been the backbone of large class of signals encountered in many modern applications as biomedical engineering, power system, radar, communication system, renewable energy and military technologies. However, statistical signal processing in complex domain are suited to only the conventional complex-valued signal processing technique for subset of complex signal known as circular (proper), which is inadequate for the generality of complex signals, as they do not rigorously exploit the statistical information available in the signal. This is because of the under-modelling of the underlying system or due to the inherent blindness of the algorithm (for example, the CNGD algorithm) to capture the full second-order statistical information available in the signal. With the limitation of the CNGD algorithm toward signal generality, an improved CNGD algorithm known as the ACNGD which is derived based on the concept of augmented complex statistic which gives optimal algorithm for the generality of signals in complex domain is introduced. The augmented CNGD has shown low Means Square Error (MSE) capabilities and have optimal performance than the conventional algorithm. To this end, a supervised complex adaptive algorithm convex combination complex nonlinear gradient descent (CC-CNGD) is developed to address the capabilities of processing the generality of complex signals (both circular and non-circular) and systems in either a noisy or a noise-free environment. Their importance in real-world application is showed through case studies. The CC-CNGD algorithm rigorously takes advantage of the fast convergence rate of the CNGD algorithm and as well exploit the low Means Square Error (MSE) of the ACNGD algorithm in order to circumvent the problem of slow convergence rate and high Mean Square Error (MSE) seen in the family of complex signal. The introduced approach is capable of facilitating real-time application, supported by numerous case studies, such as those in renewable energy. This class of algorithm performs well in either noisy or noise-free environments, the introduced approached has achieved a 20% better modelling. Fast convergence and low Mean Square Error (MSE) performance over the conventional and existing methods in the literature review. A rigorous mathematic analysis for the understanding of the proposed algorithm is shown, with ranges of simulations on both synthetic and real-world data; support the approach taken in this thesis. Electrical engineering Signal processing 2017-05 Thesis http://psasir.upm.edu.my/id/eprint/71208/ http://psasir.upm.edu.my/id/eprint/71208/1/FK%202017%2072%20-%20IR.pdf text en public masters Universiti Putra Malaysia Electrical engineering Signal processing
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Electrical engineering Signal processing
spellingShingle	Electrical engineering Signal processing Cyprian, Amadi Chukwuemena Complex-valued nonlinear adaptive filters for noncircular signals
description	Complex signal has been the backbone of large class of signals encountered in many modern applications as biomedical engineering, power system, radar, communication system, renewable energy and military technologies. However, statistical signal processing in complex domain are suited to only the conventional complex-valued signal processing technique for subset of complex signal known as circular (proper), which is inadequate for the generality of complex signals, as they do not rigorously exploit the statistical information available in the signal. This is because of the under-modelling of the underlying system or due to the inherent blindness of the algorithm (for example, the CNGD algorithm) to capture the full second-order statistical information available in the signal. With the limitation of the CNGD algorithm toward signal generality, an improved CNGD algorithm known as the ACNGD which is derived based on the concept of augmented complex statistic which gives optimal algorithm for the generality of signals in complex domain is introduced. The augmented CNGD has shown low Means Square Error (MSE) capabilities and have optimal performance than the conventional algorithm. To this end, a supervised complex adaptive algorithm convex combination complex nonlinear gradient descent (CC-CNGD) is developed to address the capabilities of processing the generality of complex signals (both circular and non-circular) and systems in either a noisy or a noise-free environment. Their importance in real-world application is showed through case studies. The CC-CNGD algorithm rigorously takes advantage of the fast convergence rate of the CNGD algorithm and as well exploit the low Means Square Error (MSE) of the ACNGD algorithm in order to circumvent the problem of slow convergence rate and high Mean Square Error (MSE) seen in the family of complex signal. The introduced approach is capable of facilitating real-time application, supported by numerous case studies, such as those in renewable energy. This class of algorithm performs well in either noisy or noise-free environments, the introduced approached has achieved a 20% better modelling. Fast convergence and low Mean Square Error (MSE) performance over the conventional and existing methods in the literature review. A rigorous mathematic analysis for the understanding of the proposed algorithm is shown, with ranges of simulations on both synthetic and real-world data; support the approach taken in this thesis.
format	Thesis
qualification_level	Master's degree
author	Cyprian, Amadi Chukwuemena
author_facet	Cyprian, Amadi Chukwuemena
author_sort	Cyprian, Amadi Chukwuemena
title	Complex-valued nonlinear adaptive filters for noncircular signals
title_short	Complex-valued nonlinear adaptive filters for noncircular signals
title_full	Complex-valued nonlinear adaptive filters for noncircular signals
title_fullStr	Complex-valued nonlinear adaptive filters for noncircular signals
title_full_unstemmed	Complex-valued nonlinear adaptive filters for noncircular signals
title_sort	complex-valued nonlinear adaptive filters for noncircular signals
granting_institution	Universiti Putra Malaysia
publishDate	2017
url	http://psasir.upm.edu.my/id/eprint/71208/1/FK%202017%2072%20-%20IR.pdf
_version_	1747812991781306368

Complex-valued nonlinear adaptive filters for noncircular signals

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