Detection of Faulty Elements From Sparse Far-Field Data in Active Phased Arrays via Machine Learning

In this contribution, we analyze machine learning-assisted solutions to tackle real-time fault detection in large-scale active phased array antennas. The challenge of integrating the circuit component nonlinearities and mutual coupling effects in fault-finding methodologies is addressed. A novel machine learning (ML) solution based on an array theory-enhanced neural network (NN) is proposed. To address the practical constraints of large array measurements, sparse far-field (FF) measurements are considered. The method is experimentally verified by applying it to fault detection in a 64-element planar phased array prototype operating at 26 GHz with far-field measurements collected by a fixed high-speed multi-probe pattern acquisition setup, the Antenna Dome. Significant improvement in fault prediction performance over a conventional Genetic Algorithm (GA) based heuristic approach with improvements of up to 40%, 25%, and 20% in predicting 8-, 4-, and 2-element faults, respectively, is demonstrated. The robustness of the proposed performance with respect to the number of 3D spatial field sampling points is shown, offering efficient diagnostics with in-field pattern sampling compatibility and low hardware complexity.