A fast failure diagnosis method based on structure features of planar arrays

Abstract

Array antennas are the basic components which are widely used in radar systems. In this paper, a fast failure diagnosis method is proposed based on structure information of planar arrays when the number of faulty elements is small. The proposed algorithm reduces the time spent locating the failed elements while maintaining the accuracy of the localization. Firstly, the sparse diagnosis model of a failure planar array is introduced. Considering the structure features of planar arrays, the vertical and horizontal elevation plane of the field pattern are used to decide the rows and columns of the failed elements, respectively. Besides, a new smaller subarray is constructed based on the rows and columns above and further diagnosed by the new measured field pattern. With the aid of theoretical analysis and simulation validation, it is shown that the proposed diagnosis method can localize faults faster compared with conventional diagnosis methods.

Model-Driven Compressive Spherical Array Sampling of Base-Station-Antenna Near Field Using Accelerated Pseudo-Skeleton Tensor Approximation

“…For instance, IoT devices are localized in bistatic passive RFID systems using 3-D radiation patterns, and an in-band full duplexing antenna with a quasi-isotropic radiation pattern is designed for smart home and in-vehicle systems [13], [14]. Aimed at avoiding the influence of faulty elements on communication systems, such as the alteration of beam directions and the increase of side lobes, radiation patterns are used to diagnose base-station antennas [15].…”