Leveraging Multiple PRF Radar for Target Detection and Sea Clutter Suppression with Deep Learning Network

Hyeonho Noh, Seong-Ryeol Park, Wonmin Cho, Kyung-Tae Kim, Hyun Jong Yang

Abstract: In this paper, we propose a deep learning-based network that combines multiple pulse repetition frequency (PRF) and velocity resolution uniformization (VRU) techniques to improve target detection in the presence of sea clutter, particularly in scenarios with limited datasets. Multiple PRF enhances the ability to distinguish between clutter and target signals by collecting data across various frequency bands, while VRU increases the uniformity of this data, thereby improving the model's generalization performance. Simulation results demonstrate that the proposed method achieves superior detection performance compared to existing techniques.

External IDs:dblp:conf/ictc/NohPCKY24