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

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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. 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. 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. 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

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

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

Available Online: 2026-04-25

Abstract

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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References(24)

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