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A New Deep Learning Framework for HF Signal Detection in Wideband Spectrogram

Detection of high frequency (HF) signal in the wideband is challenging since the HF environment is chaotic. Recent works adopt deep learning-based object detectors to capture signals in wideband spectrogram, but the task of signal detection exhibits different characteristics from that of generic object detection, which causes the classical deep learning-based detectors to have defects such as limited receptive field and prior anchor mismatch. Based on the task analysis, this letter proposes a deep learning framework which extracts features along the time axis at each frequency bin, and predicts multiple characteristics of the signal, including the center frequency and the shape attributes. The unique advantages of the framework are the utilization of all time features and no prior anchor which adapt to the slender shape of signal with a strong generalization ability. The numerical studies on simulation signal + real-world background prove the superiority of the proposed framework both in accuracy and speed.