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

SUN Dianxing; LIU Xinliang; LIU Ningbo; DING Hao; YU Hengli; SONG Guanglei

doi:10.11999/JEIT250697

Volume 48 Issue 6

Jun. 2026

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SUN Dianxing, LIU Xinliang, LIU Ningbo, DING Hao, YU Hengli, SONG Guanglei. A Physics-Constrained Deep Learning Framework for High-Fidelity Sea Clutter Generation under Small-Sample Conditions[J]. Journal of Electronics & Information Technology, 2026, 48(6): 2352-2363. doi: 10.11999/JEIT250697

Citation:

SUN Dianxing, LIU Xinliang, LIU Ningbo, DING Hao, YU Hengli, SONG Guanglei. A Physics-Constrained Deep Learning Framework for High-Fidelity Sea Clutter Generation under Small-Sample Conditions[J]. Journal of Electronics & Information Technology, 2026, 48(6): 2352-2363. doi: 10.11999/JEIT250697

Citation:

SUN Dianxing, LIU Xinliang, LIU Ningbo, DING Hao, YU Hengli, SONG Guanglei. A Physics-Constrained Deep Learning Framework for High-Fidelity Sea Clutter Generation under Small-Sample Conditions[J]. Journal of Electronics & Information Technology, 2026, 48(6): 2352-2363. doi: 10.11999/JEIT250697

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

doi: 10.11999/JEIT250697 cstr: 32379.14.JEIT250697

SUN Dianxing^1
,,
LIU Xinliang¹,
LIU Ningbo^{2
,
,},
DING Hao²,
YU Hengli²,
SONG Guanglei^{1, 2, 3}

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: The National Natural Science Foundation of China (62388102, 62101583, 61871392) , Taishan Scholars Project (tsqn202211246)

Received Date: 2025-07-24
Accepted Date: 2026-04-08
Rev Recd Date: 2026-04-08

Available Online: 2026-04-25

Publish Date: 2026-06-15

Abstract

Abstract

Objective The verification and validation of radar target detection algorithms, especially for maritime surveillance, require high-fidelity synthetic sea clutter data. However, realistic sea clutter generation under high sea-state conditions, such as Sea State 4 and above, remains challenging because sea clutter is nonstationary and non-Gaussian. Traditional statistical models often fail to capture complex time-frequency characteristics, particularly when direct measurements are difficult to obtain. To address small-sample sea clutter generation, this study proposes a physics-constrained deep learning framework with adaptive transfer learning. The aim is to generate high-quality synthetic sea clutter data that closely match measured data and provide a reliable data basis 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 transfer learning for high-fidelity sea clutter generation under small-sample conditions. The model operates in the complex domain and jointly processes in-phase and quadrature components, preserving signal orthogonality and phase relationships. An Amplitude-Phase Attention (APA) module is designed to enhance joint amplitude-phase modeling, and complex residual blocks are used to improve gradient propagation and training stability. A physics-constrained loss system, including time-frequency ridge loss and weighted Doppler-band loss, is developed to guide the generated data toward the physical characteristics of sea clutter. To reduce dependence on target-domain samples, an adaptive transfer learning mechanism based on Kullback-Leibler Divergence (KLD) is used to dynamically adjust fine-tuning and support knowledge transfer across different sea-state scenarios. Results and Discussions The proposed CVAE-WGAN framework is evaluated on measured sea clutter datasets and shows strong performance in synthetic data generation. In the source domain, namely Sea State 4, the generated data match the measured data in amplitude statistics, temporal correlation, spectral characteristics, and joint time-frequency characteristics. The probability density function-cosine similarity (PDF-CS) is 0.872 (Fig. 5, Table 1), the autocorrelation function-cosine similarity (ACF-CS) is 0.938 2 (Fig. 7, Table 1), and the spectrum root mean square error (SPEC-RMSE) is 4.537 9 dB (Fig. 6, Table 1). The joint time-frequency accuracy reaches 95.31% when |z|≤1.5 (Fig. 8, Table 1). The adaptive transfer learning mechanism is further validated by transferring the pretrained model to Sea State 5 using only 20% of the target-domain samples. The generated clutter maintains a close fit to the measured amplitude distribution (PDF-CS = 0.844 8) (Fig. 9, Table 3) and shows good autocorrelation characteristics (ACF-CS = 0.955 7) (Fig. 10, Table 4). Its spectral characteristics are also close to those of the measured data, with SPEC-RMSE = 4.498 2 dB under the 20% target-domain setting (Fig. 11, Table 4). The joint time-frequency accuracy reaches 77.8% when |z|≤1.0 and 92.18% when |z|≤1.5 (Fig. 12, Table 4). Ablation experiments show that the APA module is essential for joint amplitude-phase modeling. Removing this module reduces PDF-CS by 17.3% and increases SPEC-RMSE by 25.9% (Table 2). The sample-size sensitivity test shows that the method remains stable when the target-domain sample proportion is no less than 15%, with PDF-CS > 0.6 and joint time-frequency accuracy > 82% when |z|≤1.5 (Table 4). These results indicate that the proposed method is suitable for sea clutter generation in data-scarce scenarios. Conclusions This study presents a framework for high-fidelity sea clutter generation under small-sample conditions. The framework combines CVAE-WGAN, physics-constrained learning, and adaptive transfer learning. Guided by the proposed loss system, the model effectively captures both the statistical and physical characteristics of high sea-state clutter. The KLD-based adaptive transfer strategy improves cross-sea-state adaptability and supports high-quality data generation with limited target-domain samples. The proposed framework provides a reliable and scalable data basis for the development and testing of radar anti-interference algorithms. Future work will further optimize the method for extremely scarce samples and extend it to other nonstationary radar signal scenarios.
- Sea clutter,
- Complex-valued network,
- Transfer learning,
- Data augmentation,
- Time-frequency characteristics

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References(24)

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