Radar Emitter Signal Detection with Convolutional Neural Network

In this paper, we propose a deep convolutional neural network (CNN) based automatic detection algorithm for recognizing radar emitter signals. The algorithm leverages on the structure estimation power of deep CNN and the capability of time-frequency image processing for radio signal representation. We transform raw radio signals into time-frequency image using the Choi-Williams distribution function. We compare the proposed method with Belief Propagation (BP) and Support Vector Machine (SVM) based methods in terms of recognition accuracy versus signal-to-noise-ratio. The experiments demonstrate that the proposed CNN network with time-frequency image processing achieves very competitive results on the testing datasets.