Pulsed Signal Detection Utilizing Wavelet Analysis with a Deep Learning Approach

This paper explores the use of wavelet analysis and deep learning techniques to classify pulsed binary data over a severely noisy communications channel. Military communications need to operate in extremely harsh radio environments, which may include hostile intent to disrupt communications. Consequently, unconventional methods, such as pulsed communications, need to be investigated. Pulsed transmission techniques utilized for such channels typically result in pulses that are not easily discerned from noise and other interference. Deep learning techniques have proven advantageous in quickly and efficiently identifying minute variations in large data sets. This paper presents methods for leveraging deep learning techniques for pulsed signal detection.