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Article

Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation

by 1,2,*, 1 and 3,4
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
Algorithms 2015, 8(3), 573-589; https://0-doi-org.brum.beds.ac.uk/10.3390/a8030573
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)
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
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MDPI and ACS Style

Li, P.; Feng, J.; De Lamare, R.C. Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation. Algorithms 2015, 8, 573-589. https://0-doi-org.brum.beds.ac.uk/10.3390/a8030573

AMA Style

Li P, Feng J, De Lamare RC. Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation. Algorithms. 2015; 8(3):573-589. https://0-doi-org.brum.beds.ac.uk/10.3390/a8030573

Chicago/Turabian Style

Li, Peng; Feng, Jiao; De Lamare, Rodrigo C. 2015. "Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation" Algorithms 8, no. 3: 573-589. https://0-doi-org.brum.beds.ac.uk/10.3390/a8030573

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