A geometrical approach to robust minimum variance beamforming
Open Access
- 23 January 2004
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
This paper presents a highly efficient geometrical approach for designing robust minimum variance (RMV) beamformers against uncertainties in the array steering vector. Instead of the conventional approach of modeling the uncertainty region by a convex closed space, the proposed algorithm exploits the optimization constraint and shows that optimization only needs to be done on the intersection of a hyperplane and a second-order cone (SOC). The problem can then be cast as a second-order cone programming (SOCP) problem so as to enjoy the high efficiency of a class of interior point algorithms. A general case of modeling the uncertainties of an array using complex-plane trapezoids is investigated. The efficiency and tightness of the proposed method over other schemes are demonstrated with numerical examples.Keywords
This publication has 7 references indexed in Scilit:
- A new robust beamforming method with antennae calibration errorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Optimal array pattern synthesis using semidefinite programmingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Robust adaptive beamforming using worst-case performance optimization via Second-Order Cone programmingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric conesOptimization Methods and Software, 1999
- Applications of second-order cone programmingLinear Algebra and its Applications, 1998
- Performance analysis of the minimum variance beamformer in the presence of steering vector errorsIEEE Transactions on Signal Processing, 1996
- High-resolution frequency-wavenumber spectrum analysisProceedings of the IEEE, 1969