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Adaptive beamforming algorithm based on Simulated Kalman Filter

Kelvin Lazarus, Lazarus (2017) Adaptive beamforming algorithm based on Simulated Kalman Filter. Masters thesis, Universiti Malaysia Pahang.

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Adaptive beamforming algorithm based on Simulated Kalman Filter - Table of contents.pdf - Accepted Version

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Adaptive beamforming algorithm based on Simulated Kalman Filter - Abstract.pdf - Accepted Version

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Adaptive beamforming algorithm based on Simulated Kalman Filter - References.pdf - Accepted Version

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Abstract

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

Item Type: Thesis (Masters)
Additional Information: Thesis (Master of Science) -- Universiti Malaysia Pahang – 2017, SV: DR. NURUL HAZLINA BINTI NOORDIN, NO. CD: 11256
Uncontrolled Keywords: Adaptive beamforming; Simulated Kalman Filter
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Sufarini Mohd Sudin
Date Deposited: 31 Dec 2018 08:32
Last Modified: 31 Dec 2018 08:32
URI: http://umpir.ump.edu.my/id/eprint/23409
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