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To address the limitations of traditional statistical models in simulating the time-frequency characteristics of sea clutter, a sea clutter data generation method based on an enhanced generative adversarial network (GAN) is proposed in this paper. The complex sea clutter is decomposed into amplitude and time-frequency components, which are separately fed into a variational autoencoder-Wasserstein generative adversarial network (VAE-WGAN) for training. The outputs are then integrated to synthesize complex signals with both amplitude and phase characteristics. To enhance the model performance, a gradient penalty mechanism is introduced to constrain the Lipschitz continuity of the discriminator, effectively mitigating the mode collapse. A self-attention module is incorporated to strengthen the model’s ability to capture localized strong scattering features, such as sea spikes, significantly improving the spatiotemporal correlation of generated signals. Experiments cover sea states 2～5, with three datasets of dimensions[64,64], [128,128], and[256,256]constructed for each sea state. Twelve cross-validation trials demonstrate that the synthetic data exhibit high consistency with measured data in amplitude distribution, normalized spectrum, temporal correlation, and time-frequency characteristics. These results validate the model’s generalization capability across varying sea states and multiscale temporal scenarios.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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