Synthesis of Subarrayed Linear Array via l1-norm Minimization Compressed Sensing Method

Abstract

A novel method is proposed for synthesizing subar-rayed linear array using as few subarrays as possible. According to Compressed Sensing theory, the synthesis herein can be for- mulated as a convex problem with $l_{1}$norm minimization by de- veloping a sparse basis, which benefits from the fact that the element weighting vector is compressible and has a sparse representation. In this way, the corresponding parameters including the number of subarrays, the subarray weights and sizes can be optimized simultaneously by sequential convex optimization. The proposed method is very easy to implement and has good computational efficiency. Numerical experiments are carried out to show the performance of the proposed method.