Improving beampatterns of two-dimensional random arrays using convex optimization

Sensors are becoming ubiquitous and can be combined in arrays for source localization purposes. If classical conventional beam-forming is used, then random arrays have poor beampatterns. By pre-computing sensor weights, these beampatterns can be improved significantly. The problem is formulated in the frequency domain as a desired look direction, a frequency-independent transition region, and the power minimized in a rejection-region. Using this formulation, the frequency-dependent sensor weights can be obtained using convex optimization. Since the weights are data independent they can be pre-computed, the beamforming has similar computational complexity as conventional beamforming. The approach is demonstrated for real 2D arrays. (C) 2011 Acoustical Society of America