The problem of detecting defective turned-off elements in antenna arrays from near-field measurements is addressed. In particular, the focus here is to reduce the number of measurements in order to positively affect the acquisition time. Such an issue is achieved by adopting the recently developed warping sampling method. Two commonly antenna diagnostics methods, i.e, the Back Transformation Method (BTM) and the Matrix Method (MM), are considered in view of this new sampling strategy and compared to the usual half-wavelength sampling. In particular, in order to identify the fault locations, outcomes returned by BTM and MM undergo a detection step based on a cell-averaging CFAR (CA)-CFAR technique borrowed from the radar literature. It is shown that the warping sampling method provides performance close to the uniform half-wavelength one with a reduced number of data. Numerical simulations are carried out in order to verify the results with different fault layouts and tapered currents.

This paper aims to introduce a new strategy for the detection of faulty elements in phased array antennas. The approach takes advantage on the possibility in phased array to introduce the steering diversity. Accordingly, by collecting the radiated field over the measurement aperture while steering the beam, an approach inspired by the Time reversal MUSIC (TR-MUSIC) algorithm is exploited to achieve the diagnostics. At first, it is shown that such an algorithm allows the detection of only completely turned-off elements as long as the number of defective elements is relatively (depending on the configuration parameters) high. To overcome such a limitation, the difference model is built and the approach succeeds in detecting any failure (amplitude and phase failure) by a reduced set of data, mainly depending on the number of faulty elements. In addition, from the mere knowledge of the geometric parameters of the measurement configuration and by invoking the theory of degrees of freedom, the conditions for which the algorithm succeeded in the diagnostics are analytically derived. Furthermore, it is highlighted that the proposed algorithm achieves the diagnostics even when both the spacing between the array elements and the element factor is not a priori known. An extensive Montecarlo numerical analysis is included to show the capability of the approach to achieve a high probability of detection with a reduced set of data. Finally, full-wave simulations are also included.

In this letter, a novel near-field diagnostic method for phased array based on the MUSIC algorithm and a matrix inversion is proposed. In view of the steering diversity, the data are collected in the steering domain with a fixed probe. Numerical examples show that the proposed approach outperforms standard strategies such as Compressive sensing and Matrix method.

Millimeter-wave and sub-millimeter-wave techniques are widely used in non-destructive testing of multilayered materials due to their ability to penetrate non-conductive media and resolve dielectric stratifications. However, conventional thickness estimation methods suffer from an inherent resolution limit dictated by the available frequency bandwidth. In this paper, a MUSIC-based approach is proposed to achieve super-resolution localization of echoes in the reflective response of the structure under test. The method exploits the sparsity of the reflective response, similarly to compressive sensing approaches, while providing improved reconstruction accuracy. Moreover, the proposed strategy enables the retrieval of dielectric permittivities and layer thicknesses without resorting to complex nonlinear fitting procedures. Finally, the method operates on magnitude-only data, with phase information recovered through an interferometric holographic technique, making the proposed framework well-suited for cost-effective industrial applications.

This paper presents a novel technique for the diagnosis of faulty elements in phased array antennas. By exploiting the inherent steering diversity of phased arrays, the proposed approach overcomes the limitations imposed by truncation errors in the standard near-field Back-Transformation Method (BTM). Specifically, by reorganizing the spectra associated with each steering direction into a unified domain and leveraging data redundancy, the method effectively denoises the measurements and recovers a reliable bandwidth over the entire visible domain. Finally, an extensive Monte Carlo numerical analysis demonstrates the feasibility and enhanced accuracy of the proposed technique, highlighting its capability to achieve a high probability of detection with respect to the classical single-view approach.