Switchable Copula-guided Transformer diffusion model for detecting small sea-surface targets
- 1Naval University of Engineering
- 2People’s Armed Police Force Engineering University
Abstract
Detecting low, slow and small maritime targets remains difficult because sea clutter is non-uniform, non-stationary, non-Gaussian and often observed at extremely low signal-to-clutter ratios. Here we present a switchable Copula-guided Transformer denoising diffusion probabilistic model (SCT-DDPM) for unsupervised radar target detection. The framework replaces the conventional i.i.d. Gaussian forward prior with Copula-structured noise, uses a copula-aware weighted MSE objective to align denoising with the observed dependence structure, and combines diffusion-based anomaly attention with Transformer encoding to score target-like deviations from normal clutter. Across 13 Copula families on the IPIX radar dataset, SCT-DDPM maintained high detection performance. These results show that dependence-aware diffusion priors can improve target-clutter separability and provide a statistically grounded route for configuring diffusion models in complex maritime environments.
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BibTeX
@article{yu2026switchable,
title = {{Switchable Copula-guided Transformer diffusion model for detecting small sea-surface targets}},
author = {Chao Yu and Xuanzhu Sheng and Zhennian Luan and Shengyong Li},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-63110-0},
url = {https://doi.org/10.1038/s41598-026-63110-0},
note = {CC BY},
}
RIS
TY - JOUR
TI - Switchable Copula-guided Transformer diffusion model for detecting small sea-surface targets
AU - Chao Yu
AU - Xuanzhu Sheng
AU - Zhennian Luan
AU - Shengyong Li
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-63110-0
UR - https://doi.org/10.1038/s41598-026-63110-0
AB - Detecting low, slow and small maritime targets remains difficult because sea clutter is non-uniform, non-stationary, non-Gaussian and often observed at extremely low signal-to-clutter ratios. Here we present a switchable Copula-guided Transformer denoising diffusion probabilistic model (SCT-DDPM) for unsupervised radar target detection. The framework replaces the conventional i.i.d. Gaussian forward prior with Copula-structured noise, uses a copula-aware weighted MSE objective to align denoising with the observed dependence structure, and combines diffusion-based anomaly attention with Transformer encoding to score target-like deviations from normal clutter. Across 13 Copula families on the IPIX radar dataset, SCT-DDPM maintained high detection performance. These results show that dependence-aware diffusion priors can improve target-clutter separability and provide a statistically grounded route for configuring diffusion models in complex maritime environments.
SN - 2045-2322
ER -