A novel methodology for the robust diagnosis of large planar phased arrays is presented in this work. The developed strategy exploits the inherent sparsity of failures in large arrangements thanks to a customized Bayesian Compressive Sensing (BGS)-based approach. The detection, localization and characterization of faults is accomplished by processing noisy far-field measurements of the antenna under test (AUT) and exploiting the knowledge of the pattern radiated by the gold (error-free) antenna. Some representative numerical results are shown in order to assess the effectiveness of the proposed diagnosis technique, as well as to verify its robustness to noise occurring in real measurements.
Robust diagnosis of planar antenna arrays through a Bayesian compressive sensing approach / Gelmini, A.; Salucci, M.; Oliveri, G.; Massa, A.. - STAMPA. - (2017), pp. 1-3. (Intervento presentato al convegno APCAP 2017 tenutosi a Xi'an, China nel 16th-19th October 2017) [10.1109/APCAP.2017.8420936].
Robust diagnosis of planar antenna arrays through a Bayesian compressive sensing approach
Gelmini, A.;Salucci, M.;Oliveri, G.;Massa, A.
2017-01-01
Abstract
A novel methodology for the robust diagnosis of large planar phased arrays is presented in this work. The developed strategy exploits the inherent sparsity of failures in large arrangements thanks to a customized Bayesian Compressive Sensing (BGS)-based approach. The detection, localization and characterization of faults is accomplished by processing noisy far-field measurements of the antenna under test (AUT) and exploiting the knowledge of the pattern radiated by the gold (error-free) antenna. Some representative numerical results are shown in order to assess the effectiveness of the proposed diagnosis technique, as well as to verify its robustness to noise occurring in real measurements.File | Dimensione | Formato | |
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