Symmetry, Vol. 17, Pages 1489: Transformer-Driven GAN for High-Fidelity Edge Clutter Generation with Spatiotemporal Joint Perception

Symmetry doi: 10.3390/sym17091489

Authors:
Xiaoya Zhao
Junbin Ren
Wei Tao
Anqi Chen
Xu Liu
Chao Wu
Cheng Ji
Mingliang Zhou
Xueyong Xu

Accurate sea clutter modeling is crucial for clutter suppression in edge radar processing. On resource-constrained edge radar platforms, spatiotemporal statistics, together with device-level computation and memory limits, hinder the learning of representative clutter features. This study presents a transformer-based generative adversarial model for sea clutter modeling. The core design of this work uses axial attention to factorize self-attention along pulse and range, preserving long-range dependencies under a reduced attention cost. It also introduces a two-dimensional variable-length spatiotemporal window that retains temporal and spatial coherence across observation lengths. Extensive experiments are conducted to verify the efficacy of the proposed method with quantitative criteria, including a cosine similarity score, spectral-parameter error, and amplitude–distribution distances. Compared with CNN-based GAN, the proposed model achieves a high consistency with real clutter in marginal amplitude distributions, spectral characteristics, and spatiotemporal correlation patterns, while incurring a lower cost than standard multi-head self-attention. The experimental results show that the proposed method achieves improvements of 9.22% and 7.8% over the traditional AR and WaveGAN methods in terms of the similarity metric, respectively.

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Xiaoya Zhao www.mdpi.com