Performance analysis of sparse and small arrays

Abstract

Horizontal arrays are often used to detect/separate a weak signal and estimate its direction of arrival among many loud interfering sources and ambient noise. Conventional beamforming (CBF) is robust but suffers from fat beams and high level sidelobes. To improve performance, either high resolution beamforming is used, which has its own problems, or one needs to increase the array aperture, hence requiring more elements on the array. To reduce the cost, sparse and/or nested arrays have been proposed, such as the coprime arrays, to achieve approximately the same aperture, and hence the same beam resolution with a less number of elements. For sonar arrays, deconvolution is shown to yield an unique solution as opposed to, say, image processing. For coprime array, deconvolution algorithm is shown to yield a narrower beam width, lower sidelobe levels and higher array gain than the product or min processing, Deconvolution is shown to yield superdirectivity and supergain for a small array. This raises new research issues about optimal array configuration design.