Robust diagnosis of planar antenna arrays through a Bayesian compressive sensing approach

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.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01866587
Contributor : Andrea Massa <>
Submitted on : Monday, September 3, 2018 - 2:36:51 PM
Last modification on : Wednesday, September 5, 2018 - 1:09:53 AM

Identifiers

Citation

Angelo Gelmini, Marco Salucci, Giacomo Oliveri, Andrea Massa. Robust diagnosis of planar antenna arrays through a Bayesian compressive sensing approach. 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), Oct 2017, Xi'an, China. ⟨10.1109/APCAP.2017.8420936⟩. ⟨hal-01866587⟩

Share

Metrics

Record views

57