Object detection for signal separation with different time-frequency representations

Abstract

The task of detecting and separating multiple radio-frequency signals in a wideband scenario has attracted much interest recently, especially from the cognitive radio community. Many successful approaches in this field have been based on machine learning and computer vision methods using the wideband signal spectrogram as an input feature. YOLO and R-CNN are deep learning-based object detection algorithms that have been used to obtain state-of-the-art results on several computer vision benchmark tests. In this work, YOLOv2 and Faster R-CNN are implemented, trained and tested, to solve the signal separation task. Previous signal separation research does not consider representations other than the spectrogram. Here, specific focus is placed on investigating different time-frequency representations based on the short-time Fourier transform. Results are presented in terms of traditional object detection metrics, with Faster R-CNN and YOLOv2 achieving mean average precision scores of up to 89.3% and 88.8% respectively.