An Intelligent Recognition Method for Radar Comb Spectrum Jamming Based on Dual-Channel Deep Convolutional Network
摘要
This paper presents a deep learning method to recognize comb spectrum jamming in radar systems.Unlike traditional methods requiring manual feature extraction,our approach learns features directly from signal data.We built a dataset of radar echoes with four comb jamming types and five non-comb interference types.A dual-channel method creates 2D images preserving both magnitude and phase information from the signal spectrum.A CNN classifier with convolutional blocks,batch normalization,and dropout achieves 99.75%accuracy with 1.5%false alarm rate after only 7 training epochs.
