Convergence analysis of Bessel beamformer and its comparison with LMS in adaptive array system

Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important perf...

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Published inCompel Vol. 34; no. 3; pp. 952 - 961
Main Authors Yasin, M, Akhtar, Pervez
Format Journal Article
LanguageEnglish
Published Bradford Emerald Group Publishing Limited 01.01.2015
Subjects
Online AccessGet full text
ISSN0332-1649
2054-5606
DOI10.1108/COMPEL-07-2014-0184

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Abstract Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.
AbstractList Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.
Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.
Author Akhtar, Pervez
Yasin, M
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Cites_doi 10.1002/tee.21960
10.1109/TSP.2004.834260
10.1108/COMPEL-04-2013-0117
10.1109/78.827542
10.1109/TAP.2010.2071361
10.1002/cta.294
10.1007/s13369-014-1089-4
10.1109/ICCS.2008.4737233
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Snippet Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS)...
Purpose - The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS)...
Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS)...
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StartPage 952
SubjectTerms Adaptation
Adaptive algorithms
Adaptive systems
Algorithms
Antenna arrays
Arrays
Beamforming
Communications systems
Computer programs
Computer simulation
Convergence
Design engineering
Electrical & electronic engineering
Electrical engineering
Engineering
Expected values
Impact factors
Least mean squares algorithm
Mathematical models
Matlab
Mean square errors
Microprocessors
Orthogonality
Product differentiation
Remote sensors
Sensor arrays
Signal processing
Simulation
Wiener filtering
Wireless sensor networks
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