Deep Learning for Radar Pulse Detection

In this paper, we introduce a deep learning based framework for sequential detection of rectangular radar pulses with varying waveforms and pulse widths under a wide range of noise levels. The method is divided into two stages. In the first stage, a convolutional neural network is trained to determine whether a pulse or part of a pulse appears in a segment of the signal envelop. In the second stage, the change points in the segment are found by solving an optimization problem and then combined with previously detected edges to estimate the pulse locations. The proposed scheme is noise-blind as it does not require a noise floor estimation, unlike the threshold-based edge detection (TED) method. Simulations also show that our method significantly outperforms TED in highly noisy cases.