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.

9 pages, 2 figures. arXiv admin note: text overlap with arXiv:1505.06788

References in corpus (2)

• Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage
• Direction Finding Algorithms with Joint Iterative Subspace Optimization