A Hierarchical Convolution Neural Network Scheme for Radar Pulse Detection

The detection of radar pulses plays a critical role in passive radar systems since it provides inputs for other algorithms to localize and identify emitting targets. In this paper, we propose a hierarchical convolution neural network (CNN) to detect narrowband radar pulses of various waveforms and pulse widths at different noise levels. The scheme, named DeepIQ, takes a fixed-length segment of raw IQ samples as inputs and estimates the time of arrival (TOA) and the time of departure (TOD) of the radar pulse, if any, appearing in the segment. The estimated TOAs and TODs are then combined across segments to form a sequential detection mechanism. The DeepIQ scheme consists of sub-networks performing three different tasks: segment classification, denoising and edge detection. The proposed scheme is a full deep learning-based solution and thus, does not require any noise floor estimation process, as opposed to the commonly used Threshold-based Edge Detection (TED) methods. Simulation results show that the proposed solution significantly outperforms other schemes, especially under severe noise levels.