| 摘要: |
| 针对超宽带非视距(Non-Line-of-Sight,NLOS识别中最优特征子集选取与模型参数优化问题，提出了一种基于轻量级梯度提升机(Light Gradient Boosting Machine,LightGBM的交叉验证递归特征消除算法与Optuna参数调优相结合的NLOS识别方法。首先通过递归特征消除加交叉验证算法分析选取首径信号与总信号接收功率差值、噪声最大值等6个重要特征作为最优特征子集，之后使用Optuna调参框架优化LightGBM模型超参数。采集视距与非视距特征数据，使用支持向量机、极限梯度提升算法和参数优化后的LightGBM等模型进行训练与测试，结果表明，所选取特征具有良好区分性，参数优化后的LightGBM模型识别准确率达95.28%。 |
| 关键词: 超宽带非视距识别 轻量级梯度提升机(LightGBM 交叉验证递归特征消除算法(RFECV 超参数优化 |
| DOI：10.20079/j.issn.1001-893x.240530003 |
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| 基金项目:国网河北省电力有限公司项目(kj2021-024 |
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| A UWB Non-Line-of-Sight Recognition Method Based on LightGBM |
| LI Qian,LIU Zhuolun,SUN Xiaoyun,CHEN Yong,SONG Shiji,ZHANG Xinglong |
| (1.Shijiazhuang Power Supply Branch of State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050004,China;2.School of Electrical and Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China) |
| Abstract: |
| For the optimal feature subset selection and model parameter optimization in ultra-wideband non-line-of-sight(NLOS recognition,a new NLOS recognition method based on the cross-validation recursive feature elimination algorithm of Light Gradient Boosting Machine(LightGBM and Optuna parameter tuning is proposed.First,six important features,including the difference between the first path signal and the total received signal power,and the maximum noise,are selected as the optimal feature subset using the recursive feature elimination and cross-validation algorithm.Then,Optuna is used to optimize the hyperparameters of LightGBM model.Line-of-sight and non-line-of-sight feature data is collected,and the Support Vector Machine,Extreme Gradient Boosting algorithm,and parameter-optimized LightGBM model are trained and tested.The results demonstrate that the selected features exhibit excellent discriminative ability,with the optimized LightGBM model achieving a recognition accuracy of 95.28%. |
| Key words: UWB NLOS recognition light gradient boosting machine(LightGBM recursive feature elimination with cross validation(RFECV hyperparameter optimization |
《电讯技术》编辑部 