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Algorithms 2015, 8(3), 573-589; doi:10.3390/a8030573

Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation

1
School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Ningliu Road 219, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing 210044, China
3
CETUC, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
4
Department of Electronics, University of York, Heslington, York YO10 5DD, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Erchin Serpedin
Received: 18 May 2015 / Revised: 28 July 2015 / Accepted: 29 July 2015 / Published: 5 August 2015
(This article belongs to the Special Issue Algorithms for Sensor Networks)
View Full-Text   |   Download PDF [359 KB, uploaded 5 August 2015]   |  

Abstract

In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent dimensionality reduction based on an iterative optimization with steering vector perturbation (IOVP) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank reduction matrix and an adaptive beamformer. The optimized rank reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust IOVP design. The proposed robust IOVP beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed IOVP algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity. View Full-Text
Keywords: adaptive filters; beamforming algorithms; reduced rank adaptive filters; beamforming algorithms; reduced rank
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, P.; Feng, J.; de Lamare, R.C. Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation. Algorithms 2015, 8, 573-589.

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