A mixing vector based an affine combination of two adaptive filters for sensor array beamforming

In this paper, a novel beamformer for adaptive combination of two adaptive filters is proposed for interference mitigation of sensor array. The proposed approach adaptively combines two individual filters by coefficient weights vector instead of one scale parameter and takes the constraint of affine...

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Main Authors: Lu, S. T., Sun, J. P., Wang, G. H., Lu, Yilong.
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/84784
http://hdl.handle.net/10220/10896
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author Lu, S. T.
Sun, J. P.
Wang, G. H.
Lu, Yilong.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Lu, S. T.
Sun, J. P.
Wang, G. H.
Lu, Yilong.
author_sort Lu, S. T.
collection NTU
description In this paper, a novel beamformer for adaptive combination of two adaptive filters is proposed for interference mitigation of sensor array. The proposed approach adaptively combines two individual filters by coefficient weights vector instead of one scale parameter and takes the constraint of affine combination into consideration rather than previous studies. Due to the more degrees of freedom offered by the mixing vector, the proposed beamformer significantly improves the convergence and tracking performances of the combined filter under both stationary and non-stationary environments, respectively. Based on the generalized sidelobe canceller (GSC) structure, the optimal mixing vector is derived by Lagrange method, and then several new effective iterative algorithms are developed for its updating in practical implementation. Furthermore, theoretical discussions of the convergent performances and complexities of the proposed iterative algorithms are also investigated to verify the feasibility of the proposed beamformer. Moreover, the proposed methods in application of beamforming for interference mitigation of antenna array are simulated based space-time processing technique. When compared to existing methods, the proposed approach exhibits faster convergence rate and higher output signal to interference plus noise ratio (SINR). Its good behavior is illustrated through simulation results.
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spelling ntu-10356/847842020-03-07T13:57:29Z A mixing vector based an affine combination of two adaptive filters for sensor array beamforming Lu, S. T. Sun, J. P. Wang, G. H. Lu, Yilong. School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering In this paper, a novel beamformer for adaptive combination of two adaptive filters is proposed for interference mitigation of sensor array. The proposed approach adaptively combines two individual filters by coefficient weights vector instead of one scale parameter and takes the constraint of affine combination into consideration rather than previous studies. Due to the more degrees of freedom offered by the mixing vector, the proposed beamformer significantly improves the convergence and tracking performances of the combined filter under both stationary and non-stationary environments, respectively. Based on the generalized sidelobe canceller (GSC) structure, the optimal mixing vector is derived by Lagrange method, and then several new effective iterative algorithms are developed for its updating in practical implementation. Furthermore, theoretical discussions of the convergent performances and complexities of the proposed iterative algorithms are also investigated to verify the feasibility of the proposed beamformer. Moreover, the proposed methods in application of beamforming for interference mitigation of antenna array are simulated based space-time processing technique. When compared to existing methods, the proposed approach exhibits faster convergence rate and higher output signal to interference plus noise ratio (SINR). Its good behavior is illustrated through simulation results. Published version 2013-07-03T02:31:29Z 2019-12-06T15:51:08Z 2013-07-03T02:31:29Z 2019-12-06T15:51:08Z 2012 2012 Journal Article Lu, S. T., Sun, J. P., Wang, G. H., & Lu, Y. L. (2012). A mixing vector based an affine combination of two adaptive filters for sensor array beamforming. Progress In Electromagnetics Research, 122, 361-387. 1070-4698 https://hdl.handle.net/10356/84784 http://hdl.handle.net/10220/10896 10.2528/PIER11090204 en Progress In electromagnetics research © 2012 EMW Publishing. This paper was published in Progress In Electromagnetics Research and is made available as an electronic reprint (preprint) with permission of EMW Publishing. The paper can be found at the following official DOI: [http://dx.doi.org/10.2528/PIER11090204]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Lu, S. T.
Sun, J. P.
Wang, G. H.
Lu, Yilong.
A mixing vector based an affine combination of two adaptive filters for sensor array beamforming
title A mixing vector based an affine combination of two adaptive filters for sensor array beamforming
title_full A mixing vector based an affine combination of two adaptive filters for sensor array beamforming
title_fullStr A mixing vector based an affine combination of two adaptive filters for sensor array beamforming
title_full_unstemmed A mixing vector based an affine combination of two adaptive filters for sensor array beamforming
title_short A mixing vector based an affine combination of two adaptive filters for sensor array beamforming
title_sort mixing vector based an affine combination of two adaptive filters for sensor array beamforming
topic DRNTU::Engineering::Electrical and electronic engineering
url https://hdl.handle.net/10356/84784
http://hdl.handle.net/10220/10896
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