A Total-Variation Sparseness-Promoting Method for the Synthesis of Contiguously Clustered Linear Arrays

TL;DR

This paper introduces a novel total-variation compressive sensing method for synthesizing contiguous clustered linear arrays, optimizing feed network excitations to match user-defined patterns with improved clustering.

Contribution

It presents a new TV-CS formulation and an alternating direction algorithm for array synthesis, demonstrating advantages over existing subarraying techniques.

Findings

Effective clustering of array elements demonstrated

Method outperforms some state-of-the-art techniques

Limitations identified in realistic scenarios

Abstract

By exploiting an innovative total-variation compressive sensing (TV-CS) formulation, a new method for the synthesis of physically contiguous clustered linear arrays is presented. The computation of the feed network excitations is recast as the maximization of the gradient sparsity of the excitation vector subject to matching a user-defined pattern. The arising TV-CS functional is then optimized by means of a deterministic alternating direction algorithm. A selected set of representative numerical results, drawn from a wide validation, is reported to illustrate the potentialities and the limitations of the proposed approach when clustering arrays of both ideal and realistic antenna elements. Comparisons with some competitive state-of-the-art subarraying techniques are performed as well.