Intelligently Leveraging Multi-Channel Image Processing Neural Networks for Multi-View Co-Channel Signal Detection

The evolution of technology and gadgets has led to a significant increase in the number of transmitted signals, making RF sensing more complex than ever. Challenges such as signal interference and the lack of prior information about all signal parameters further complicate the task. To address this challenge, researchers have explored machine learning and deep learning approaches to generalize solutions for real-world sensing problems. In this thesis, we focus on two key issues in RF signal detection using deep learning. Firstly, we tackle the problem of increasing signal detection coverage by utilizing multiple resolution eigengram images derived from a bank of channelizers. These channelizers, varying in size, are adept at sensing different types of signals, such as low duration or low bandwidth signals. Channelizer deconfliction is a known challenge in RFML. We use YOLO, a deep learning algorithm, to deconflict the outputs from different channelizers to avoid overreporting. YOLO's ability to handle three channels makes it ideal for our study as we also use three channelizers. While our approach is not dependent on YOLO, it provides a good testing ground for this study. To address signal overlap, we utilize an eigengram image capturing the overlap region between signals. By overlaying this eigengram onto the original, we create a composite image highlighting the overlap. We train another YOLO model using two channels, one for each eigengram, enabling detection even with over 50 percent overlap. This work is versatile and promising, extending to other signal visualizations. It has significant potential for wireless industry applications and sets the stage for further RFML research.