Minimum-Complexity Failure Correction in Linear Arrays via Compressive Processing

TL;DR

This paper introduces a novel compressive processing method to efficiently correct failures in linear arrays by minimizing the number of excitation adjustments needed to restore the original pattern.

Contribution

It proposes the Minimum-Complexity Failure Correction approach that reformulates failure correction to reduce corrections using compressive processing, unlike traditional methods.

Findings

Effective reduction in the number of corrections compared to existing techniques

Successful restoration of original patterns in numerical test cases

Competitive performance demonstrated through extensive testing

Abstract

Given an array with defective elements, failure correction (FC) aims at finding a new set of weights for the working elements so that the properties of the original pattern can be recovered. Unlike several FC techniques available in the literature, which update all the working excitations, the Minimum-Complexity Failure Correction (MCFC) problem is addressed in this paper. By properly reformulating the FC problem, the minimum number of corrections of the whole excitations of the array is determined by means of an innovative Compressive Processing (CP) technique in order to afford a pattern as close as possible to the original one (i.e., the array without failures). Selected examples, from a wide set of numerical test cases, are discussed to assess the effectiveness of the proposed approach as well as to compare its performance with other competitive state-of-the-art techniques in terms of both pattern features and number of corrections.