Enhancing Microwave System Health Assessment Using Artificial Neural Networks

Abstract

A novel method is presented for remotely assessing microwave system health using environmental monitoring sensors that employ a low-power random noise radar with artificial neural network-based machine learning processing. The method expands prior stimulated unintended radiated emission (SURE) research in [M. W. Lukacs, A. J. Zeqolari, P. J. Collins, and M. A. Temple, “ ‘RF-DNA fingerprinting’ for antenna classification,” IEEE Antennas Wireless Propag. Lett. , vol. 14, pp. 1455–1458, 2015] by adding a new hybrid expert-empirical concept-forming technique called matched filter replication (MFR), the outputs of which are used in ensemble learning. Also by comparison with baseline performance, classification improvement is demonstrated using a single iteration of MFR ensemble learning that improves antenna termination state classification by $\%C_\Delta >\text{19}\%$ . A nested ensemble learning architecture is also introduced that enables classification of truly unknown devices with no learner in the ensemble being trained. This exploits the concept of multiple iterations using the MFR process by establishing an enlarged hypothesis space that is subsequently collapsed to form a final classification decision. The new SURE architecture enables the assessment of unknown device capabilities using a network trained on selected primitive traits (in this case, reflectivity).