基于稀疏采样传播数据和决策树算法的蒸发波导反演

蒸发波导的超视距传播特性是影响海上无线电系统性能的重要因素,准确预测蒸发波导是进行系统 评估的基础。文中提出一种基于稀疏采样传播数据和决策树轻量梯度提升机(Light Gradient Boosting Machine, LightGBM)算法的蒸发波导反演方法。首先,采用抛物方程方法仿真得到稀疏采样传播数据并构建训练集和测试 集;其次,使用决策树LightGBM 算法搭建反演模型,通过不断调参改进模型的精度以达到较高的反演准确度;最后, 调用训练好的LightGBM 模型进行蒸发波导反演,并对反演结果的概率分布进行了分析。结果表明,基于稀疏采样 传播数据的蒸发波导反演方法能够有效且快速地实现蒸发波导反演,为海上蒸发波导预测提供了一种新途径。

Research on Inversion of Evaporation Duct Based on Sparse Sampling Propagation Data and Decision Tree Algorithm

The over-the-horizon propagation characteristics of evaporation duct is an important factor affecting the marine radio system. Therefore, accurate prediction of evaporation duct is the basis of system evaluation. In this thesis, an evaporative duct inversion method based on sparse sampling propagation data and Light Gradient Boosting Machine (LightGBM) of decision tree algorithm is proposed. Firstly, the training set and the test set are constructed by the sparse sampling propagation data obtained by the parabolic equation. Secondly, the decision tree LightGBM algorithm is used to build the inversion model, and the accuracy of the model is improved by adjusting the parameters to achieve higher inversion accuracy. Finally, the trained Light- GBM model is used for evaporation duct inversion, and the probability distribution of inversion results is analyzed. The result shows that the evaporation duct inversion method based on sparse sampling propagation data can effectively and quickly realize the evaporation duct inversion, which provides a new way for the prediction of evaporation duct at sea.