title: Improving beampatterns of two-dimensional random arrays using convex optimization.
abstract: Sensors are becoming ubiquitous and can be combined in arrays for source localization purposes. If classical conventional beamforming 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.
authors.affiliation: Marine Physical Laboratory, Scripps Institution of Oceanography, La Jolla, CA 92093-0238, USA. gerstoft@ucsd.edu
authors.forename: Peter
authors.initials: P
authors.lastname: Gerstoft
authors.forename: William S
authors.initials: WS
authors.lastname: Hodgkiss
mesh_terms: D000162:Acoustics; D000388:Air; D003195:Computer Communication Networks; D004566:Electrodes; D006801:Humans; D008954:Models, Biological; D009792:Oceans and Seas; D013017:Sound Localization; D013061:Speech Acoustics