The performance of hybrid adaptive beamforming algorithm for jammers suppression

Chern, Shiunn-Jang; Sung, Chih‐Yuan

doi:10.1109/8.318643

Cited by 5 publications

(2 citation statements)

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“…Where the superscript is denoted as the Hermitian operation and is the number of array elements. The output signal is given by (3) In the method of exponentially weighted least square, we choose the weights at time , so as to minimize the cost function that consists of the sum of weighted output power (4) where parameter,…”

Section: B the Optimal Solution Of The Lc-iqrd-rls Beamformermentioning

confidence: 99%

“…However, under certain circumstances, the conventional Frost's beamforming algorithm may have some problem associated with the performance degradation in multiple jammers environment. To circumvent this drawback, in [3] the adaptive transformed domain normalized LMS algorithm with discrete cosine transform (DCT) and discrete Harley transform (DHT) for broadband adaptive array structure were proposed to improve the nulling capability as well as convergence speed. Also, in [4] the linearly constrained robust fast least square (FLS) beamforming algorithm was suggested to achieve better performance for broadband beamformer.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive linearly constrained inverse QRD-RLS beamforming algorithm for moving jammers suppression

Chern

Chang

2002

IEEE Trans. Antennas Propagat.

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In this paper, a general, linearly constrained (LC) recursive least squares (RLS) array-beamforming algorithm, based on an inverse QR decomposition, is developed for suppressing the moving jammers, efficiently. In fact, by using the inverse QR decomposition-recursive least squares (QRD-RLS) algorithm approach, the lease-squares (LS) weight vector can be computed without back substitution and is suitable to be implemented using the systolic array to achieve fast convergence and good numerical properties. The merits of this new constrained algorithm is verified by evaluating the performance, in terms of the learning curve, to investigate the convergence property and numerical efficiency, and the output signal to interference and noise ratio. We show that our proposed algorithm outperforms the conventional linearly constrained LMS (LCLMS) algorithm, and the one using the fast linear constrained RLS algorithm and its modified version.

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Section: B the Optimal Solution Of The Lc-iqrd-rls Beamformermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%