网络安全与数据治理2025，Vol.44Issue(9)：51-58,65,9.DOI:10.19358/j.issn.2097-1788.2025.09.008
HF和波动参数辅助的优化XGBoost室内定位方法
Optimization of XGBoost indoor positioning method with HF and fluctuation parameter assistance
摘要
Abstract
Aiming at the problem that the received signal strength measurement data in complex indoor environments contain noise which makes them show fluctuation and leads to low positioning accuracy,an optimized extreme gradient boosting(XGBoost)in-door positioning method based on hybrid filtering(HF)and fluctuation parameters assistance is proposed.Firstly,the HF method is used to optimize the data subset,reduce the influence of noise,and obtain the initial database;in addition,considering that the fluctuation can't be completely eliminated,the fluctuation parameter that can reflect the degree of data change is introduced;sec-ondly,for the performance of the XGBoost algorithm is susceptible to the influence of the initial parameter,the particle swarm(PSO)algorithm is used for optimization of the parameter,and the fluctuation parameter and optimized data are used as inputs to train the algorithm to generate the localization model;finally,the target point information is input into the model to complete the position estimation,and the point data is saved into the database to complete the update.The experimental results show that com-pared with the traditional algorithms,the algorithm in this paper has a good localization effect,and the localization accuracy is im-proved by 9.2%,14.1%,and 18.45%in the range of 1 m,2 m,and 3 m,respectively.关键词
室内定位/混合滤波/波动参数/粒子群算法/XGBoostKey words
indoor positioning/hybrid filtering/fluctuation parameters/PSO algorithm/XGBoost分类
信息技术与安全科学引用本文复制引用
刘高辉,凌凤智..HF和波动参数辅助的优化XGBoost室内定位方法[J].网络安全与数据治理,2025,44(9):51-58,65,9.基金项目
国家自然科学基金(61671375) （61671375）
