Convergence Characteristics of the Adaptive Array Using RLS Algorithm

Futoshi ASANO; Yoiti SUZUKI; Toshio SONE

Convergence Characteristics of the Adaptive Array Using RLS Algorithm

Futoshi ASANO, Yoiti SUZUKI, Toshio SONE

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The convergence characteristics of the adaptive beamformer with the RLS algorithm are analyzed in this paper. In case of the RLS adaptive beamformer, the convergence characteristics are significantly affected by the spatial characteristics of the signals/noises in the environment. The purpose of this paper is to show how these physical parameters affect the convergence characteristics. In this paper, a typical environment where a few directional noises are accompanied by background noise is assumed, and the influence of each component of the environment is analyzed separately using rank analysis of the correlation matrix. For directional components, the convergence speed is faster for a smaller number of noise sources since the effective rank of the input correlation matrix is reduced. In the presence of background noise, the convergence speed is slowed down due to the increase of the effective rank. However, the convergence speed can be improved by controlling the initial matrix of the RLS algorithm. The latter section of this paper focuses on the physical interpretation of this initial matrix, in an attempt to elucidate the mechanism of the convergence characterisitics.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.1 pp.148-158

Publication Date: 1997/01/25

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Category: Digital Signal Processing

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Futoshi ASANO, Yoiti SUZUKI, Toshio SONE, "Convergence Characteristics of the Adaptive Array Using RLS Algorithm" in IEICE TRANSACTIONS on Fundamentals, vol. E80-A, no. 1, pp. 148-158, January 1997, doi: .
Abstract: The convergence characteristics of the adaptive beamformer with the RLS algorithm are analyzed in this paper. In case of the RLS adaptive beamformer, the convergence characteristics are significantly affected by the spatial characteristics of the signals/noises in the environment. The purpose of this paper is to show how these physical parameters affect the convergence characteristics. In this paper, a typical environment where a few directional noises are accompanied by background noise is assumed, and the influence of each component of the environment is analyzed separately using rank analysis of the correlation matrix. For directional components, the convergence speed is faster for a smaller number of noise sources since the effective rank of the input correlation matrix is reduced. In the presence of background noise, the convergence speed is slowed down due to the increase of the effective rank. However, the convergence speed can be improved by controlling the initial matrix of the RLS algorithm. The latter section of this paper focuses on the physical interpretation of this initial matrix, in an attempt to elucidate the mechanism of the convergence characterisitics.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_1_148/_p

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@ARTICLE{e80-a_1_148,
author={Futoshi ASANO, Yoiti SUZUKI, Toshio SONE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Convergence Characteristics of the Adaptive Array Using RLS Algorithm},
year={1997},
volume={E80-A},
number={1},
pages={148-158},
abstract={The convergence characteristics of the adaptive beamformer with the RLS algorithm are analyzed in this paper. In case of the RLS adaptive beamformer, the convergence characteristics are significantly affected by the spatial characteristics of the signals/noises in the environment. The purpose of this paper is to show how these physical parameters affect the convergence characteristics. In this paper, a typical environment where a few directional noises are accompanied by background noise is assumed, and the influence of each component of the environment is analyzed separately using rank analysis of the correlation matrix. For directional components, the convergence speed is faster for a smaller number of noise sources since the effective rank of the input correlation matrix is reduced. In the presence of background noise, the convergence speed is slowed down due to the increase of the effective rank. However, the convergence speed can be improved by controlling the initial matrix of the RLS algorithm. The latter section of this paper focuses on the physical interpretation of this initial matrix, in an attempt to elucidate the mechanism of the convergence characterisitics.},
keywords={},
doi={},
ISSN={},
month={January},}

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TY - JOUR
TI - Convergence Characteristics of the Adaptive Array Using RLS Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 148
EP - 158
AU - Futoshi ASANO
AU - Yoiti SUZUKI
AU - Toshio SONE
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 1997
AB - The convergence characteristics of the adaptive beamformer with the RLS algorithm are analyzed in this paper. In case of the RLS adaptive beamformer, the convergence characteristics are significantly affected by the spatial characteristics of the signals/noises in the environment. The purpose of this paper is to show how these physical parameters affect the convergence characteristics. In this paper, a typical environment where a few directional noises are accompanied by background noise is assumed, and the influence of each component of the environment is analyzed separately using rank analysis of the correlation matrix. For directional components, the convergence speed is faster for a smaller number of noise sources since the effective rank of the input correlation matrix is reduced. In the presence of background noise, the convergence speed is slowed down due to the increase of the effective rank. However, the convergence speed can be improved by controlling the initial matrix of the RLS algorithm. The latter section of this paper focuses on the physical interpretation of this initial matrix, in an attempt to elucidate the mechanism of the convergence characterisitics.
ER -