高海况海杂波数据迁移学习生成方法

孙殿星; 刘新亮; 刘宁波; 丁昊; 于恒力; 宋光磊

doi:10.11999/JEIT250697

高海况海杂波数据迁移学习生成方法

doi: 10.11999/JEIT250697 cstr: 32379.14.JEIT250697

孙殿星¹,
刘新亮¹,
刘宁波^2, ,,
丁昊²,
于恒力²,
宋光磊^{1, 2, 3}

1.
哈尔滨工程大学哈尔滨 150001
2.
海军航空大学烟台 264001
3.
中国航天科技创新研究院北京 100032

基金项目: 国家自然科学基金(62388102, 62101583, 61871392)和泰山学者工程(tsqn202211246)

详细信息

作者简介:
孙殿星：男，博士，副教授，研究方向为雷达信号处理、雷达数据处理及雷达抗干扰技术

刘新亮：男，硕士研究生，研究方向为海杂波仿真和特征分析

刘宁波：男，博士，教授，研究方向为雷达信号智能处理、海上目标探测技术

丁昊：男，博士，副教授，研究方向为海杂波特性认知与抑制、海杂波中目标检测

于恒力：男，博士，副教授，研究方向为雷达海上目标检测识别

宋光磊：男，博士，研究员，研究方向为卫星综合信息处理，军事智能，卫星网络

通讯作者:
刘宁波　lnb198300@163.com

中图分类号: TN959.72
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- HTML全文浏览量: 3
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- 被引次数: 0
出版历程
- 收稿日期: 2025-07-24
- 修回日期: 2026-04-08
- 录用日期: 2026-04-08
- 网络出版日期: 2026-04-25

A Physics-Constrained Deep Learning Framework for High-Fidelity Sea Clutter Generation under Small-Sample Conditions

1.
Harbin Engineering University, Harbin 150001, China
2.
Naval Aviation University, Yantai 264001, China
3.
China Academy of Aerospace Science and Technology Innovation, Beijing 100032, China

Funds: XXXXXXXXXXXXXXXXX

摘要

摘要: 高海况海杂波数据在雷达目标检测性能验证中需求迫切，直接生成具有良好时频特征的高海况海杂波数据难度大。针对这一问题，该文提出融合复数变分自编码对抗网络(Complex Variational Autoencoder Wasserstein Generative Adversarial Network, CVAE-WGAN)与迁移学习的创新框架。通过构建复数域深度架构，通过复数卷积核保留信号正交特性，结合幅度-相位注意力模块(Amplitude-Phase Attention, APA)增强时频特征提取，并引入复数残差块优化梯度传播。设计物理约束导向的损失函数体系，利用时频脊损失捕捉非平稳能量演化轨迹，通过多普勒频带损失强化雷达相干处理特性；提出基于Kullback-Leibler散度(Kullback-Leibler Divergence, KLD)的自适应迁移机制——在源域预训练后，对目标域动态解冻高分布差异层实现跨场景知识迁移。实验验证生成数据在四级海况幅度统计特性、时间相关性和时频特征上均高度逼近实测数据；迁移至五级海况(20%目标域样本)后，仍保持优异的幅度分布与自相关特性，时频物理特征还原能力接近源域水平。消融研究证实APA对相位-幅度联合建模起决定性作用，样本量敏感性测试表明方法在15%目标数据量下性能稳定。该框架通过复数域物理约束与自适应迁移的协同创新，显著提升小样本海杂波生成质量，为雷达抗干扰算法提供可靠数据基础，极端稀缺样本场景的适应性优化将是后续重点。
- 海杂波 /
- 复值神经网络 /
- 迁移学习 /
- 数据增强 /
- 时频特性
Abstract: Objective The verification and validation of radar target detection algorithms, particularly in maritime surveillance, heavily relies on the availability of high-fidelity synthetic sea clutter data. However, generating realistic sea clutter under high sea-state conditions (e.g., Sea State 4 and above) is a significant challenge due to the non-stationary and non-Gaussian nature of the signal. Traditional statistical models often fail to capture the complex time-frequency characteristics of such data, especially when direct measurement is difficult or unavailable. A novel framework is proposed that combines a complex-valued generative adversarial network with physics-constrained learning and an adaptive transfer learning mechanism to address the issue of small-sample sea clutter generation. The primary goal is to develop a robust and efficient method for generating high-quality synthetic sea clutter data that closely mimics real-world conditions, thereby providing a reliable data foundation for the development and testing of advanced radar systems. Methods The proposed framework integrates a Complex Variational Autoencoder Wasserstein Generative Adversarial Network (CVAE-WGAN) with a transfer learning strategy to address the challenge of generating high-fidelity sea clutter data under small-sample conditions. The model operates in the complex domain to jointly process in-phase and quadrature components, preserving the orthogonality and phase relationships of the signal. A Magnitude-Phase Attention (APA) module is introduced to enhance the joint modeling of amplitude and phase, while complex residual blocks are designed to improve gradient propagation and training stability. A physics-constrained loss function system, comprising a time-frequency ridge loss and a Doppler band loss, is implemented to guide the generation process to align with the physical characteristics of sea clutter. To handle data scarcity, an adaptive transfer learning mechanism based on Kullback-Leibler Divergence (KLD) is employed to dynamically adjust the model during fine-tuning in target domains, enabling efficient knowledge transfer across different sea-state scenarios. Results and Discussions The performance of the proposed CVAE-WGAN framework is evaluated using real-world sea clutter datasets, demonstrating its effectiveness in generating high-fidelity synthetic data. In the source domain (Sea State 4), the generated data closely matches real measurements in terms of amplitude statistics (PDF-CS = 0.872) (Fig. 5), temporal correlation (ACF-CS = 0.9382) (Fig. 7), and time-frequency characteristics (SPEC-RMSE = 4.5379 dB) (Fig. 6). The time-frequency ridge accuracy reaches 95.2% (|z|≤1) (Fig. 10). The adaptive transfer learning mechanism is validated by applying the pre-trained model to a more challenging scenario (Sea State 5) with only 20% of the target domain samples. The generated clutter maintains a strong fit to the empirical amplitude distribution (PDF-CS = 0.8448) (Fig. 11, Table 2) and exhibits good autocorrelation properties (ACF-CS = 0.9557) (Fig. 12, Table 2), with time-frequency ridge accuracy at 95.24% (∣z∣≤1) (Fig. 14, Table 2). Ablation studies reveal that the Magnitude-Phase Attention (APA) module is critical for joint amplitude and phase modeling, as its removal significantly degrades performance (e.g., PDF-CS drops 17.3%, SPEC-RMSE increases 35.0%) (Table 1). The method proves stable even with as little as 15% of the target data (PDF-CS > 0.6, Z=1 > 82%) (Table 3), underscoring its suitability for data-scarce environments. Conclusions This study presents a novel framework for generating high-fidelity sea clutter data under small-sample conditions, combining a complex-valued generative adversarial network with physics-constrained learning and an adaptive transfer learning mechanism. The proposed CVAE-WGAN model, guided by a sophisticated loss function system, demonstrates a strong capability to capture both the statistical and physical properties of high sea-state environments. The integration of the KLD-based transfer learning mechanism significantly enhances the model's adaptability, enabling high-quality data generation even with limited target domain samples. By addressing the challenge of small-sample sea clutter generation, this framework provides a reliable and robust data foundation for the development and testing of advanced radar anti-clutter and anti-jamming algorithms. Future work focuses on further optimizing the framework for extreme data scarcity and exploring its application in other non-stationary radar signal scenarios.
- Sea Clutter /
- Deep Generative Model /
- Transfer Learning /
- Complex-Valued Network /
- Physics-Constrained

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图 1 CVAE-WGAN模型框架

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图 2 网络参数设计

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图 3 复数残差块

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图 4 APA结构示意图

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图 5 四级海况幅度概率密度函数对比

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图 6 四级海况归一化频谱对比

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图 7 四级海况自相关系数对比

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图 8 四级海况生成数据时频特性z分数准确率

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图 9 迁移学习幅度分布验证

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图 12 迁移学习时频准确率验证

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图 10 迁移学习时间自相关特性验证

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图 11 迁移学习频谱特性验证

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表 1 模型对比实验表

模型变体	PDF-CS	SPEC-RMSE	ACF-CS	平均功率误差	Z=1.5
CVAE-WGAN	0.872	4.5379dB	0.9382	0.0035	95.31%
WGAN-GP	0.7654	6.215dB	0.8321	0.0421	35.94%
相对提升幅度	↑13.9%	↓26.9%	↑14.0%	↑3.86%	↑59.37%

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表 2 模型消融实验对照表(相对性能百分比)

模型变体	PDF-CS	SPEC-RMSE	ACF-CS	Z=0.5	Z=1.5
完整模型	100%	100%	100%	100%	100%
无注意力模型	82.7%	74.1%	87.4%	74.8%	86.8%
无残差模型	92.3%	84.9%	96.1%	86.6%	93.6%
全基础模型	78.3%	65.8%	84.8%	62.9%	79.1%

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表 3 五级海况迁移生成性能对比

评估指标	计算方法	平均值	评价标准
PDF-CS	余弦相似度(公式12)	0.8448	越接近1越好
ACF-CS	余弦相似度	5.4357	越接近1越好
SPEC-RMSE	对数PSD均方根误差(dB)	5.4357	越小越好
时频准确率(Z=1)	\|z\|≤0.5的样本比例	77.8%	越大越好
时频准确率(Z=1.5)	\|z\|≤1的样本比例	92.18%	越大越好

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表 4 迁移学习不同样本量对性能影响分析

数据集大小	PDF-CS	SPEC-RMSE	ACF-CS	Z=1	Z=1.5
20%	0.8448	4.4982	0.9557	77.8%	92.18%
15%	0.6144	5.4357	0.8889	60.94%	82.54%
10%	0.5039	5.8023	0.8025	56.25%	80.95%
5%	0.2837	6.4458	0.7107	45.31%	66.67%

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