Minimum-Complexity Failure Correction in Linear Arrays via Compressive Processing

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.

Failure correction of antenna arrays using convex optimization

“…18 All the numerical simulations were performed in MATLAB R2021b on a desktop with a 3.8 GHz CPU and 32 GB RAM. and Zardi et al, 16 we adjust the excitation of some elements to zero to model the failure of some elements. In this case, the number of failure elements is M = 2, and the indexes are {2, 5} .…”

“…This section presents some numerical evaluation cases that have been simulated to verify the superiority in terms of correction consistency, correction complexity, and convergence speed of the proposed algorithm. We adopt the excitation data provided in the GA 14 and reproduce the MCFC algorithm 16 for comparison. The L 1‐norm minimization calculations involved are completed by the interior point method 18 .…”

“…For the first numerical example, we select a linear omnidirectional antenna array weighted by −30 dB DC synthesis; the array consists of 32 elements with a spacing of $$\lambda \unicode{x02215}2$$. Following the same case in Yeo and Lu 14 and Zardi et al, 16 we adjust the excitation of some elements to zero to model the failure of some elements. In this case, the number of failure elements is $$M=2$$, and the indexes are $$\{2,5\}$$.…”

“…9 Notice that, none of the above methods take using fewer intact elements to compensate the impaired pattern into consideration, which leads to the complexity and high cost of reconstructed T/R components. Taking the correction complexity into account, Zardi et al proposed the Minimum-Complexity Failure Correction (MCFC) method, 16 which aims to minimize the number of reconstruction elements through compression processing. Such a method achieved the minimum complexity of reconstruction on the premise of maintaining good performance of the corrected pattern; as a result, the time cost of T/R module reset can be reduced and the error caused by the redundant element adjustments can be decreased.…”

On the far‐field sampling strategies for reliable Bayesian compressive diagnosis of planar arrays

“…The availability of an effective/reliable array diagnosis tool to remotely detect failures in the transmit/receive modules (TRMs) is highly desirable in those applications (e.g., satellite communications) where the manual replacement of damaged radiators is impractical or too expensive 1–8 . Indeed, the identification of failures, starting from the collected samples of the radiated far‐field (FF) pattern of the antenna under test (AUT), is fundamental for triggering automatic recalibration procedures on the healthy radiators 9–12 …”