Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Parameter extraction of radar signals is an important but challenging task in electronic warfare. In the modern electromagnetic environment, the radiation sources greatly increase, causing different radar signals to overlap, making the parameter extraction of radar signals difficult. Meanwhile, using radar signal parameter extraction methods that are not suitable for dealing with overlapping signals can lead to serious errors in this case. To address this, we propose a parameter extraction network for overlapping radar signals using modulation recognition-guided semantic segmentation. Specifically, we first design an encoder–decoder to segment overlapping radar signals, which uses channel rearrangement and modulation type filtering to increase the accuracy of segmentation. In this encoder–decoder, channel rearrangement is an optimization of convolution operation, aiming to increase the perceptual field while reducing feature information loss. And modulation type filtering can convert the results of semantic segmentation into masks corresponding to each radar signal, increasing the accuracy of segmentation. After the encoder–decoder, signal segmentation masks are obtained. Then, we compress these segmentation masks in the time and frequency domains, and extract the span of them to achieve accurate extraction of the pulsewidth and bandwidth of each radar signal. The experiments validate the feasibility of the proposed method.