A Geometrical Approach to
Robust Minimum Variance Beamforming
A Geometrical Approach to
Robust Minimum Variance Beamforming
N. Wong, T.-S. Ng, and V. Balakrishnan
In Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing,
Hong Kong, April 2003
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.
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