Hybridization of Bayesian Compressive Sensing and Array Dilation Technique for Synthesis of Linear Isophoric Sparse Antenna Arrays
Ashutosh Kedar, Pratap Vangol, A Mahesh
Abstract
This article presents a deterministic hybrid algorithm combining the merits of Bayesian compressive sensing (BCS) and array dilation technique (ADT) for the synthesis of aperiodic and isophoric (equally fed) linear sparse array antennas (LSAAs). The hybrid technique overcomes the shortcomings of BCS, i.e., nonuniform amplitude taper which results into a lower taper efficiency, using hybridization with ADT resulting into an isophoric LSAA, thereby improving the taper efficiency. The proposed algorithm can be used to synthesize arbitrary antenna array patterns as per the reference pattern while complementing the constraints on the aperture length and the desired thinning levels. The hybrid algorithm provides a computationally efficient and deterministic technique for synthesizing LSAAs with low sidelobe level (SLL) and demonstrates a wide angular scanning with minimal loss in directivity and SLL over the scan volume. The results demonstrated are comparable or better than the prior literature on LSAAs.