A second-order cone bounding algorithm for robust minimum variance beamforming

Authors:
Ngai Wong;Venkataramanan Balakrishnan;Tung-Sang Ng
Affiliations:
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong
Venue:
Switching and Learning in Feedback Systems
Year:
2003

Citing 8
Cited 0

Semidefinite programming

SIAM Review
Array Signal Processing: Concepts and Techniques

Array Signal Processing: Concepts and Techniques
Robust minimum variance beamforming

IEEE Transactions on Signal Processing
Performance analysis of the minimum variance beamformer in thepresence of steering vector errors

IEEE Transactions on Signal Processing
On robust Capon beamforming and diagonal loading

IEEE Transactions on Signal Processing
Antenna array pattern synthesis via convex optimization

IEEE Transactions on Signal Processing
Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem

IEEE Transactions on Signal Processing
Optimal array pattern synthesis using semidefinite programming

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a geometrical approach for designing robust minimum variance (RMV) beamformers against steering vector uncertainties. Conventional techniques enclose the uncertainties with a convex set; the antenna weights are then designed to minimize the maximum array output variance over this set. In contrast, we propose to cover the uncertainty by a second-order cone (SOC). The optimization problem, with optional robust interference rejection constraints, then reduces to the minimization of the array output variance over the intersection of the SOC and a hyperplane. This is cast into a standard second-order cone programming (SOCP) problem and solved efficiently. We study the computationally efficient case wherein the uncertainties are embedded in complex-plane trapezoids. The idea is then extended to arbitrary uncertainty geometries. Effectiveness of the proposed approach over other schemes and its fast convergence in signal power estimation are demonstrated with numerical examples.