Adaptive beamforming algorithm based on Simulated Kalman Filter

Adaptive beamforming is a technique used to steer the radiation pattern towards the desired signal and cancel out any interference signal by finding the appropriate weights for every element in an array antenna, to achieve maximum signal to interference plus noise ratio (SINR). There are many method...

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Main Author:	Kelvin Lazarus, Lazarus
Format:	Thesis
Language:	English
Published:	2017
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/23409/1/Adaptive%20beamforming%20algorithm%20based%20on%20Simulated%20Kalman%20Filter.wm.pdf
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id	my-ump-ir.23409
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spelling	my-ump-ir.234092023-01-27T02:49:14Z Adaptive beamforming algorithm based on Simulated Kalman Filter 2017-12 Kelvin Lazarus, Lazarus TK Electrical engineering. Electronics Nuclear engineering Adaptive beamforming is a technique used to steer the radiation pattern towards the desired signal and cancel out any interference signal by finding the appropriate weights for every element in an array antenna, to achieve maximum signal to interference plus noise ratio (SINR). There are many methods to perform adaptive beamforming and one of the method is to use metaheuristic algorithm, to estimate the weights for individual elements in an array. Over the years, various metaheuristic algorithms have been applied to adaptive beamforming. Some of the metaheuristic algorithms have been modified from the original algorithms to improve the algorithms performance in adaptive beamforming application. A new metaheuristic algorithm named Simulated Kalman Filter (SKF), is inspired by the estimation capabilities of Kalman filter, has not been applied to adaptive beamforming application. Therefore, this research presents the first-time application of SKF algorithm to adaptive beamforming. The SKF algorithm, however, often converge prematurely at local optimum due to lack of exploration, preventing it from finding better solution. A modified version of the SKF algorithm, named Opposition-Based SKF (OBSKF), introduced by K. Zakwan, applies Opposition-Based Learning method to improve the exploration capabilities of SKF algorithm. Moreover, a new modified version of the SKF algorithm named SKF with Modified Measurement (SKFMM) is introduced to further improve the exploration capabilities of SKF algorithm by modifying the measurement-update equation. The SKF, OBSKF and SKFMM is applied to an array antenna with 10 elements arranged linearly with 0.5 2017-12 Thesis http://umpir.ump.edu.my/id/eprint/23409/ http://umpir.ump.edu.my/id/eprint/23409/1/Adaptive%20beamforming%20algorithm%20based%20on%20Simulated%20Kalman%20Filter.wm.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Electrical & Electronic Engineering Nurul Hazlina, Noordin
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Nurul Hazlina, Noordin
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Kelvin Lazarus, Lazarus Adaptive beamforming algorithm based on Simulated Kalman Filter
description	Adaptive beamforming is a technique used to steer the radiation pattern towards the desired signal and cancel out any interference signal by finding the appropriate weights for every element in an array antenna, to achieve maximum signal to interference plus noise ratio (SINR). There are many methods to perform adaptive beamforming and one of the method is to use metaheuristic algorithm, to estimate the weights for individual elements in an array. Over the years, various metaheuristic algorithms have been applied to adaptive beamforming. Some of the metaheuristic algorithms have been modified from the original algorithms to improve the algorithms performance in adaptive beamforming application. A new metaheuristic algorithm named Simulated Kalman Filter (SKF), is inspired by the estimation capabilities of Kalman filter, has not been applied to adaptive beamforming application. Therefore, this research presents the first-time application of SKF algorithm to adaptive beamforming. The SKF algorithm, however, often converge prematurely at local optimum due to lack of exploration, preventing it from finding better solution. A modified version of the SKF algorithm, named Opposition-Based SKF (OBSKF), introduced by K. Zakwan, applies Opposition-Based Learning method to improve the exploration capabilities of SKF algorithm. Moreover, a new modified version of the SKF algorithm named SKF with Modified Measurement (SKFMM) is introduced to further improve the exploration capabilities of SKF algorithm by modifying the measurement-update equation. The SKF, OBSKF and SKFMM is applied to an array antenna with 10 elements arranged linearly with 0.5
format	Thesis
qualification_level	Master's degree
author	Kelvin Lazarus, Lazarus
author_facet	Kelvin Lazarus, Lazarus
author_sort	Kelvin Lazarus, Lazarus
title	Adaptive beamforming algorithm based on Simulated Kalman Filter
title_short	Adaptive beamforming algorithm based on Simulated Kalman Filter
title_full	Adaptive beamforming algorithm based on Simulated Kalman Filter
title_fullStr	Adaptive beamforming algorithm based on Simulated Kalman Filter
title_full_unstemmed	Adaptive beamforming algorithm based on Simulated Kalman Filter
title_sort	adaptive beamforming algorithm based on simulated kalman filter
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical & Electronic Engineering
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/23409/1/Adaptive%20beamforming%20algorithm%20based%20on%20Simulated%20Kalman%20Filter.wm.pdf
_version_	1783732063708381184

Adaptive beamforming algorithm based on Simulated Kalman Filter

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