Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Author: Cyprian, Amadi Chukwuemena
Format: Thesis
Language:English
Published: 2017
Online Access:http://psasir.upm.edu.my/id/eprint/71208/1/FK%202017%2072%20-%20IR.pdf
http://psasir.upm.edu.my/id/eprint/71208/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.71208
record_format eprints
spelling my.upm.eprints.712082019-08-29T08:35:17Z http://psasir.upm.edu.my/id/eprint/71208/ Complex-valued nonlinear adaptive filters for noncircular signals 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. 2017-05 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/71208/1/FK%202017%2072%20-%20IR.pdf Cyprian, Amadi Chukwuemena (2017) Complex-valued nonlinear adaptive filters for noncircular signals. Masters thesis, Universiti Putra Malaysia.
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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
author Cyprian, Amadi Chukwuemena
spellingShingle Cyprian, Amadi Chukwuemena
Complex-valued nonlinear adaptive filters for noncircular signals
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
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/71208/1/FK%202017%2072%20-%20IR.pdf
http://psasir.upm.edu.my/id/eprint/71208/
_version_ 1643839839145033728
score 13.211869

Similar Items