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Toward dynamic radar signal sorting via gramian dimensionality elevation fusion CNN
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DOI：10.1016/j.phycom.2026.103057.png)
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Abstract
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Radar signal sorting (RSS) is a crucial component within electronic reconnaissance systems, aiming to separate multiple radar pulses from an interlaced pulse stream. However, with the increasing complexity of the electromagnetic environment, radar signals are exhibiting diversified and dynamic characteristics. Traditional RSS techniques struggle to handle the nonlinear variations inherent in complex modulated signals and are susceptible to noise interference and spurious pulses. To address these challenges, deep learning technologies, notably Convolutional Neural Networks (CNNs), offer a promising solution for sorting signals with dynamic parameters. Nevertheless, single-modality CNNs lack the capacity to simultaneously capture intricate feature representations and process sequential data effectively. Motivated by this limitation, this paper proposes a novel network named Gramian-based CNN Fusion Network (GCF-Net) designed for efficient RSS in dynamic environments. Initially, comprehensive sorting datasets are established by modeling seven types of Pulse Repetition Interval (PRI) sequences. Subsequently, a dual-branch recognition and classification network is developed based on the Gramian Angular Difference Field (GADF) representation. Finally, experiments are conducted using the proposed GCF-Net. Experimental results demonstrate that GCF-Net achieves a high recognition rate of 92.4%. Moreover, it exhibits exceptional robustness in highly dynamic environments and significantly outperforms other deep-learning-based RSS methods.
Keywords:
Radar signal sorting
Dimensionality elevation
Graph convolutional network
Feature fusion
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