Performance assessment of a machine-learning-derived digital RF communication classifier

Steve T. Kacenjar; Aaron Dant; Ronald Neely

doi:10.1117/12.2557422

21 April 2020 Performance assessment of a machine-learning-derived digital RF communication classifier

Steve T. Kacenjar, Aaron Dant, Ronald Neely

Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 1141311 (2020) https://doi.org/10.1117/12.2557422
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

A study is performed to gauge the effectiveness of training a Machine Learning (ML) System for Automatic Modulation Classification (AMC) to accurately identify several diverse digital communication transmission types occurring across the High Frequency (HF) Radio Frequency (RF) spectrum. This study uniquely uses Software Defined Radio (SDR) Power Spectral Density (PSD) waterfall signatures to help classify nine common types of amateur radio digital communication modes. Such an approach provides an alternative to more traditional In-phase/Quadrature (IQ) methods which can require large training sets. LeNet and ResNet Convolutional Neural Network (CNN) models are examined. Training/validation sets sensitivities are examined through Monte Carlo methods. Additionally, performances are examined in terms of confusion matrices as a function of Signal-to-Noise Ratio (SNR).

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Citation Download Citation

Steve T. Kacenjar, Aaron Dant, and Ronald Neely "Performance assessment of a machine-learning-derived digital RF communication classifier", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 1141311 (21 April 2020); https://doi.org/10.1117/12.2557422

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