Random Thinning of Segmented Annular Arrrays

Nowadays, two-dimensional (2D) arrays design is base in a square matrix (SM) distribution of elements, which requires a pitch of ! /2 in order to eliminate grating lobes. From this condition, a 2D array will contain between 1500 and 16000 elements, which are much higher than the number of channels of present image systems. A well-known technique for reducing the number of active elements is based on randomly eliminating a part of the elements from the aperture. Segmented-annular (SA) arrays constitute an alternative to SM arrays for the generation of volumetric images, because they have lower periodicity than squared patterns, and therefore they allow increasing the inter-element distance up to ! or even further. SA arrays, then, the number of elements are divided by four with respect to SM arrays using the full aperture. However, this number is still a challenge for the existent technology, so requiring thinning the aperture. In this work, a thinning technique based on random sparse is applied to segmented-annular arrays and squared-matrix arrays. Several random layouts are applied to equivalent SM and SA arrays (equivalent arrays have the same active area and the same number of elements, which are of similar size and aspect ratio), and the comparative results are then theoretically analyzed in the paper.