基于主成分降维的海面散射系数快速预测方法  

A fast prediction method for sea surface scattering coefficient based on principal component analysis dimensionality reduction

作　　者：刘悦 董春雷 孟肖[1] 郭立新[1] LIU Yue;DONG Chunlei;MENG Xiao;GUO Lixin(School of Physics,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学,西安710071

出　　处：《电波科学学报》2025年第1期21-28,共8页Chinese Journal of Radio Science

基　　金：国家自然科学基金(62231021,62301401);中央高校基本科研业务费专项资金(QTZX22161)。

摘　　要：海面电磁散射特性与海浪参数、雷达参数等多种影响因素存在复杂的依赖关系,传统大场景海面电磁散射预测模型在面临多参数高维度映射时容易出现过拟合问题,选择合适的降维方法和模型参数是提高模型性能的有效手段。本文提出了一种基于主成分分析(principal components analysis,PCA)降维的海面电磁散射快速预测方法。首先,利用文氏海谱和海面电磁散射模型构建后向散射系数仿真数据集;然后,引入PCA法降低仿真参数维度,提取主要特征;最后,基于最小二乘支持向量回归机(least squares support vector regression,LSSVR)建立非线性回归模型,输入降维数据进行预测,并评估预测结果的精度。通过对比不同降维比例的预测结果,分析了主成分降维对模型性能的影响。结果表明,对仿真参数进行适当降维能够显著增加模型精度,提升模型的解释能力。当降维比例为25%左右时模型精度达到最优,当降维比例大于40%时模型精度显著下降,不利于海面电磁散射预测。The electromagnetic scattering characteristics of sea surface have complex dependencies on various influencing factors,such as sea wave parameters,radar parameters,etc.The traditional electromagnetic scattering prediction models for the large-scale sea surface tend to suffer from overfitting problems when facing multi-parameter high-dimensional mapping.Choosing appropriate dimensionality reduction methods and model parameters is an efficient way to improve the model performance.Therefore,this paper proposes a fast prediction method for sea surface electromagnetic scattering based on principal component analysis(PCA)dimensionality reduction.Firstly,the backscattering coefficient data set is constructed by using the Wen’s spectrum and multi-scale electromagnetic scattering model of the sea surface.Then,the PCA method is introduced to reduce the dimension of the simulation parameters and extract the main features.Finally,a nonlinear regression model based on least squares support vector regression(LSSVR)machine is established,and the dimensionality reduction data is inputted for prediction and the accuracy of the prediction results is evaluated.By comparing the prediction results of different dimensionality reduction ratios,the influence of principal component dimensionality reduction on the model performance is analyzed.The results show that reducing the dimension of the simulation parameters appropriately can significantly increase the accuracy and enhance the interpretability.When the dimensionality reduction ratio is about 25%,the model accuracy reaches the optimum.When the dimensionality reduction ratio is greater than 40%,the model accuracy decreases significantly,which is not conducive to sea surface electromagnetic scattering prediction.

关 键 词：主成分分析(PCA) 海面电磁散射预测 最小二乘支持向量回归机(LSSVR) 半确定性面元法 参数降维 

分 类 号：TN958[电子电信—信号与信息处理]