Phased array antenna diagnosis from amplitude-only data using parallel deep learning models

There is a need for a reliable diagnostic process that can provide the information required to maintain optimal operating conditions of phased array antennas. The phase and amplitude information can be retrieved from the complex radiated field. However, phase measurement is laborious and costly at high frequencies. To overcome this challenge, it is of great relevance to create diagnostics methods that solely utilize the amplitude data. The shape and characteristics of the radiated field of a phased array antenna are predicated on the number of operating elements in the array. This implies that a failed element in the array will change the nature of the radiated pattern. We can locate a failed element using a learning algorithm to map the location of a failed element to the radiated field. We propose a technique for finding failed elements using only amplitude data by dividing the array into multiple subarrays. Each subarray is trained with dedicated deep convolutional neural networks for faulty element identification. We evaluated the proposed approach using simulated data for 20 x 20 and 30 x 30 array structures. The results demonstrate that the proposed approach can effectively diagnose and locate failed elements in a phased array antenna using amplitude-only data.

Automated summary

A reliable diagnostic process is needed to ensure optimal operating conditions of phased array antennas. Phase measurement is difficult and costly at high frequencies, so diagnostic methods solely utilizing amplitude data are relevant. Failed elements in the array can be located by mapping their location to the radiated field using a learning algorithm. Our proposed approach uses amplitude-only data and deep neural networks to identify failed elements, and it effectively diagnoses and locates them in simulated array structures of various sizes.