Study of Efficient Robust Adaptive Beamforming Algorithms Based on Shrinkage Techniques
Abstract
This paper proposes low-complexity robust adaptive beamforming (RAB) techniques based on shrinkage methods. We firstly briefly review a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) batch algorithm to estimate the desired signal steering vector mismatch, in which the interference-plus-noise covariance (INC) matrix is also estimated with a recursive matrix shrinkage method. Then we develop low complexity adaptive robust version of the conjugate gradient (CG) algorithm to both estimate the steering vector mismatch and update the beamforming weights. A computational complexity study of the proposed and existing algorithms is carried out. Simulations are conducted in local scattering scenarios and comparisons to existing RAB techniques are provided.
Cite
@article{arxiv.1512.01601,
title = {Study of Efficient Robust Adaptive Beamforming Algorithms Based on Shrinkage Techniques},
author = {H. Ruan and R. C. de Lamare},
journal= {arXiv preprint arXiv:1512.01601},
year = {2015}
}
Comments
9 pages, 2 figures. arXiv admin note: text overlap with arXiv:1505.06788