Abstract
ABSTRACT The Compressive Sensing (CS) theory has been widely studied as it requires only a small number of measurements to identify faulty elements of antenna arrays. However, the model for linear antenna array fault diagnosis based on CS is much sensitive to model mismatch and the identification results usually turn out to be much worse than expected in the practical situation. In this study, the model mismatch for linear antenna array fault diagnosis is first analyzed and then a CS model calibration method is proposed to improve the performance of the CS-based linear antenna array fault diagnostic method by solving a convex optimization model. Numerical results demonstrate that the calibrated CS model is much more accurate in identifying faulty antenna elements under model mismatch and noise than the traditional one.
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