A recovery performance study of compressive sensing methods on antenna array diagnosis from near-field measurements

Antenna testing consists locating the potential defaults from radiated field measurements. It has been established in literature, that compressive sensing methods provide faster results in failure detection from smaller number of measurement data compared to the traditional back-propagation mechanisms. Compressive sensing (CS) methods require a priori measurement of failure-free reference array and require small number of measurements for diagnosis. However, there are conflicting reports in literature regarding the choice of appropriate CS method, and there is no sufficient comparison study to justify which one is a better choice under a very harsh condition. In this study, recovery performance test of CS methods for the diagnosis of antenna array from few near-field measured data under various signal-to-noise ratios (SNRs) is presented. Specifically, we tested three prominent regularization procedures: total variation (TV), mixed l₁/l₂ norm, and minimization of the l₁ in solving diagnosis problems in antenna array. Linear system that relates the difference between near-field measured data from reference antenna (RA) array and array under test (AUT), and the difference that exist between coefficients of RA and the AUT, is solved by the three compressive sensing regularization methods. Numerical experiment of a 10 × 10 rectangular waveguide array under realistic noise scenario, operating at 10 GHz is used to conduct the test. Minimization l₁ technique is more robust to additive data noise. It exhibits better diagnosis at 20 dB and 10 dB SNR, making it a better candidate for noisy measured data as compared to other techniques.