Paper Info
Title
Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation
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.
Year
DOI
Venue
2015
10.3390/a8030573
ALGORITHMS
Keywords
Field
DocType
adaptive filters,beamforming algorithms,reduced rank
Dimensionality reduction,Adaptive beamformer,Robust optimization,Artificial intelligence,Adaptive filter,Beamforming,Mathematical optimization,Subspace topology,Vector optimization,Algorithm,Mathematics,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
8
3
1999-4893
Citations 
PageRank 
References 
1
0.36
13
Authors
3
Name
Order
Citations
PageRank
Peng Li18111.75
Jiao Feng221.72
Rodrigo C. de Lamare31461179.59