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rlms algorithm for Fixed or Adaptive Beamforming

RLMS Algorithm for Fixed or Adaptive Beamforming

引用

9th IEEE Malaysia International Conference on Communications

作者： Srar, Jalal Abdulsayed Chung, Kah-Seng Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9781424455324

This paper presents a flexible method of achieving either fixed or self-adaptive antenna beamforming. It involves the use of the array image factor A(d)', which interfaces the Recursive Least Squares (RLS) and Least Mean Squares (LMS) sections in cascade to form the rlms beamforming algorithm. It is shown that an accurate fixed beam can be obtained by simply setting the elements of A(d)' with prescribed values corresponding to the required direction. Moreover, the beam direction can also be made adaptive to automatically track the target signal. In this case, a simple method is described for estimating the element values of A(d)' based on the estimated RLS output signal and tap weights. Computer simulation results verify these two modes of operation of the rlms beamforming algorithm. Furthermore, the convergence performance of rlms is shown to be quite insensitive to variations in SNR of the input signal as well as step sizes associated with the RLS and LMS sections.

关键词： Fixed beam rlms algorithm adaptive antenna array beam forming

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Performance of rlms algorithm in Adaptive Array Beam Forming

Performance of RLMS Algorithm in Adaptive Array Beam Forming

引用

11th IEEE Singapore International Conference on Communication Systems

作者： Srar, Jalal Abdulsayed Chung, Kah-Seng Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9781424424238

This paper examines the performance of an adaptive linear array employing the new rlms algorithm, which consists of a recursive least square (RLS) section followed by a least mean square (LMS) section. The performance measures used are output and input signal-to-interference plus noise ratios (SINR), side lobe level (SLL), and Delta SINRo, as a function of the direction of arrival of the interfering signal. Computer simulation results show that the performance of rlms is superior to either the RLS or LMS based on these measures, particularly when operating with low input SINR.

关键词： RLS algorithm LMS algorithm rlms algorithm adaptive antenna array beam forming

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rlms algorithm for Fixed or Adaptive Beamforming

RLMS Algorithm for Fixed or Adaptive Beamforming

引用

IEEE Malaysia International Conference on Communications

作者： Jalal Abdulsayed Srar Kah-Seng Chung Department of Electrical and Computer Engineering Curtin University of Technology

ISBN: (纸本)9781424455317;9781424455324

This paper presents a flexible method of achieving either fixed or self-adaptive antenna beamforming. It involves the use of the array image factor A'_d, which interfaces the Recursive Least Squares (RLS) and Least Mean Squares (LMS) sections in cascade to form the rlms beamforming algorithm. It is shown that an accurate fixed beam can be obtained by simply setting the elements of A'_d with prescribed values corresponding to the required direction. Moreover, the beam direction can also be made adaptive to automatically track the target signal. In this case, a simple method is described for estimating the element values of A'_d based on the estimated RLS output signal and tap weights. Computer simulation results verify these two modes of operation of the rlms beamforming algorithm. Furthermore, the convergence performance of rlms is shown to be quite insensitive to variations in SNR of the input signal as well as step sizes associated with the RLS and LMS sections.

关键词： Fixed beam rlms algorithm Adaptive antenna array beam forming fixed beam Adaptive Beam forming Fixed Restless Legs Syndrome input signal operation mode Analog-digital conversion Beamforming beam direction Levamisole learning management system

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Analysis of the rlms Adaptive Beamforming algorithm Implemented with Finite Precision

Analysis of the RLMS Adaptive Beamforming Algorithm Implemen...

引用

16th Asia-Pacific Conference on Communications (APCC)

作者： Srar, Jalal Abdulsayed Chung, Kah-Seng Mansour, Ali 7th October Univ Dept Elect & Elect Engn Misurata Libya Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9781424481293

This paper studies the influence of the use of finite wordlength on the operation of the rlms adaptive beamforming algorithm. The convergence behavior of rlms, based on the minimum mean square error (MSE), is analyzed for operation with finite precision. Computer simulation results verify that a wordlength of nine bits is sufficient for the rlms algorithm to achieve performance close to that provided by full precision. The performance measures used include residual MSE, rate of convergence, error vector magnitude (EVM), and beam pattern. Based on all these measures, it is shown that the rlms algorithm outperforms other earlier algorithms, such as least mean square (LMS), recursive least square (RLS), modified robust variable step size (MRVSS) and constrained stability LMS (CSLMS).

关键词： rlms algorithm array beamforming fixed-point arithmetic

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Adaptive Array Beam Forming Using a Combined RLS-LMS algorithm

Adaptive Array Beam Forming Using a Combined RLS-LMS Algorit...

引用

14th Asia-Pacific Conference on Communications

作者： Srar, Jalal Abdulsayed Chung, Kah-Seng Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9784885522321

A new adaptive algorithm, called rlms, which combines the use of Recursive Least Square (RLS) and Least Mean Square (LMS), is proposed for array beam forming. The convergence of the rlms algorithm is analyzed, in terms of mean square error, in the presence of additive white Gaussian noise. Computer simulation results show that the convergence performance of rlms is superior to either RLS or LMS operating on its own. Furthermore, the convergence of rlms is quite insensitive to changes in either signal-to-noise ratio, or the initial value of the input correlation matrix for the RLS section, or the step size adopted for the LMS section.

关键词： RLS algorithm LMS algorithm rlms algorithm array processing adaptive array beam forming

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