A Mask-Assisted Radar Signal Sorting Method Based on Digitized PDW and U1DADM

To address the challenge of effectively sorting radar signals with identical modulation types and overlapping parameter ranges in complex electromagnetic environments using traditional methods, this paper proposes a one-dimensional convolutional neural network, U1DADM, based on semantic segmentation. This network extracts deep semantic information from preprocessed digitized signals and introduces a masking strategy for overlapping pulse regions. The proposed digitized data processing method achieves joint modeling of intra-pulse and inter-pulse features. The core module of the U1DADM network, adaptive dilated convolution, achieves multi-scale feature fusion and long-range feature dependency modeling through dynamic receptive field adjustment. The masking strategy mitigates data defects and assists the network in focusing on repetitive pulse regions. Experimental results indicate that the proposed method surpasses similar deep learning methods and traditional sorting methods under various data conditions. Furthermore, it exhibits strong robustness and generalization ability even under harsh conditions with spurious and missed pulses, achieving high-precision radar signal sorting.