Antenna Array Diagnosis via Smart Sensing of Electromagnetics with Learnable Data Acquisition and Processing

This paper presents a low signal-to-noise ratio (SNR) real-time antenna array diagnosis method with remarkable reduction in measurements via smart sensing of electromagnetic (EM) with learnable data acquisition and processing. Previous techniques such as matrix inversion, exhaustive search, and genetic algorithm are generally time consuming in data acquisition, and/or require complex reconstruction algorithms for a successful data post-processing. This forces limitations on previous techniques, making them inefficient and ineffective for real time array diagnosis. Addressing these shortcomings, we introduce EM sensing by developing data-driven learnable data acquisition with the integration of a data-driven learned data processing pipeline. As a result, measurement technique is jointly learned with a matching post-processing, towards diagnostic operation, consequently giving us opportunity to conduct real-time and accurate antenna array diagnosis, with a remarkable reduction in measurements at low SNR. We illustrate the effectiveness of the developed method using an HFSS-based 10 × 10-array of waveguide in practical noise scenario. The results obtained show the efficiency of the developed approach, even at low SNR.