| 摘要: |
| 针对经典算法建模海杂波时无法同时满足海杂波多个统计特性所造成的拟合精度缺失以及无法按类别条件可控生成的问题，结合U-Net的生成能力与复值神经网络处理电磁领域内复杂非线性问题的潜力，通过采用各种复值网络层将模型推广至复数域，同时引入无分类器模块，建立一种对输入条件可解释的映射机制，提出了一种复值引导扩散模型(Complex-valued Guided Diffusion Model,CVG-DM。该模型旨在利用海杂波的同向(In-phase，I、正交(Quadrature,Q路复值基带信号以及挖掘海杂波与对应杂波背景下强目标的关联，从而在目标有无条件下实现模型的可控生成，最后在幅度分布、时空相关性、非线性特性、多普勒谱方面评价生成结果。仿真实验证明，CVG-DM可按条件实现海杂波数据增广，仿真杂波能同时兼顾以上五方面统计特性，比基于实数网络的评价指标更加完备，保真度进一步提高。 |
| 关键词: 海杂波模拟 扩散模型 复值神经网络 无分类器引导 |
| DOI：10.20079/j.issn.1001-893x.240717001 |
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| 基金项目: |
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| A Complex-valued Guided Diffusion Model Data Augmentation Algorithm for Radar Sea Clutter |
| LIANG Taining,YANG Haocheng,KUANG Huaxing |
| (1.National Key Laboratory of Electromagnetic Effect and Security on Marine Equipment,Nanjing 211153,China;2.The 724th Research Institute of China State Shipbuilding Corporation Limited,Nanjing 211153,China;3.School of Information Science and Engineering,Southeast University,Nanjing 210096,China) |
| Abstract: |
| In response to challenges in sea clutter modeling within the classical algorithms,including the lack of fitting accuracy due to the inability to satisfy multiple statistical characteristics simultaneously and the limitations in controllably generating accurate class-based results,combining the generative power of U-Net with the potential of complex-valued neural networks to deal with complex nonlinear problems in the electromagnetic domain,a novel approach is proposed.This approach integrates complex-valued network layers and a classifier-free guidance module,establishing an interpretable mapping mechanism for input conditions,resulting in complex-valued guided diffusion model(CVG-DM.This model is centered on the direct utilization of the complex-valued baseband signals from the In-phase and Quadrature(IQ path of sea clutter,as well as the exploration of the relationship between sea clutter and strong targets in the background.This enables controlled generation of the model under varying conditions of target presence or absence,and assessment based on amplitude distribution,temporal and spatial correlation,nonlinear characteristics,and Doppler spectrum.Simulation experiment validates CVG-DM搒 capability in realizing sea clutter data augmentation under varying conditions.The simulated clutter can simultaneously take into account above five statistical properties,surpassing the completeness of real number network-based evaluation metrics and further enhancing fidelity. |
| Key words: sea clutter simulation diffusion model complex-valued neural network classifier-free guidance |
