Attention Mechanism Optimized Generative Adversarial Networks and Their Application in Sea Clutter Simulation

ZHANG Su-kai; CHEN Peng; DONG Zi-ying; WANG Wei

doi:10.12263/DZXB.20250475

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Attention Mechanism Optimized Generative Adversarial Networks and Their Application in Sea Clutter Simulation

PAPERS | 更新时间：2025-12-27

- Attention Mechanism Optimized Generative Adversarial Networks and Their Application in Sea Clutter Simulation
- ACTA ELECTRONICA SINICA Vol. 53, Issue 9, Pages: 3345-3357(2025)
- 作者机构：
  
  长安大学信息工程学院，陕西西安 710064
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62341133);Interdisciplinary Team Building Program for Young Scholars, Chang’an University(3001042400922);Fundamental Research Funds for the Central Universities, Chang’an University(300102244203;300102243401)
- DOI：10.12263/DZXB.20250475
  CLC： TP391;
- Received：05 June 2025，
  
  Accepted：09 September 2025，
  
  Published：25 September 2025
- 稿件说明：
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张苏恺, 陈鹏, 董紫颖, 等. 基于注意力机制优化的生成对抗网络及其在海杂波模拟中的应用[J]. 电子学报, 2025, 53(09): 3345-3357.

ZHANG Su-kai, CHEN Peng, DONG Zi-ying, et al. Attention Mechanism Optimized Generative Adversarial Networks and Their Application in Sea Clutter Simulation[J]. Acta Electronica Sinica, 2025, 53(09): 3345-3357.
张苏恺, 陈鹏, 董紫颖, 等. 基于注意力机制优化的生成对抗网络及其在海杂波模拟中的应用[J]. 电子学报, 2025, 53(09): 3345-3357. DOI：10.12263/DZXB.20250475

ZHANG Su-kai, CHEN Peng, DONG Zi-ying, et al. Attention Mechanism Optimized Generative Adversarial Networks and Their Application in Sea Clutter Simulation[J]. Acta Electronica Sinica, 2025, 53(09): 3345-3357. DOI：10.12263/DZXB.20250475

摘要

针对在复杂海况下，雷达海杂波模拟所面临的全局特征建模不足、多模态生成能力受限以及评估体系单一等挑战，本文提出了一种基于多头自注意力机制增强的高保真生成对抗网络（Self-Attention HIgh FIdelity Generative Adversarial Network，SA-HIFIGAN）.该模型在生成器与判别器中嵌入了多头自注意力模块，以增强对海杂波长程时空相关性的建模能力，并设计了具有分类功能的多尺度与多周期判别器结构.同时，本文构建了一个融合分布相似性、频谱误差和统计稳定性的混合评估体系，实现了对生成杂波的多维质量控制.通过采用X波段雷达实测数据集进行实验，验证了模型在幅度概率密度、功率谱密度和时空相关性等指标上的有效性.实验结果显示，SA-HIFIGAN在上述指标上与实测数据高度吻合，不仅能根据海况等级生成对应特性的杂波数据，还在综合评分上优于深度卷积生成对抗网络（Deep Convolutional Generative Adversarial Network，DCGAN）和变分自编码器（Variational Auto-Encoder，VAE）等现有的杂波生成方法.

Abstract

To address the challenges in radar sea clutter simulation under complex sea conditions

including insufficient global feature modeling

limited multimodal generation capability

and a simplistic evaluation system

this paper proposes a generative adversarial network enhanced by multi-head self-attention mechanisms self-attention high-fidelity generative adversarial network (SA-HIFIGAN). The model incorporates multi-head self-attention modules in both the generator and discriminator to strengthen the modeling of long-range spatiotemporal correlations in sea clutter. Additionally

a multi-scale and multi-period discriminator structure with classification functionality is designed. Furthermore

this paper constructs a hybrid evaluation system integrating distribution similarity

spectral error

and statistical stability

achieving multidimensional quality control for generated clutter. Experiments conducted using X-band radar field-measured datasets validate the model’s effectiveness in metrics such as amplitude probability density

power spectral density

and spatiotemporal correlation. The results demonstrate that SA-HIFIGAN achieves high consistency with measured data across these metrics. Not only can it generate clutter data with characteristics corresponding to sea state levels

but it also outperforms existing clutter generation methods like deep convolutional generative adversarial network (DCGAN) and variational auto-encoder (VAE) in comprehensive scoring.

关键词

Keywords

references

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