基于CSI-XGBoost的高精度WiFi室内定位算法

考虑到室内环境的复杂性和多径效应对WiFi指纹定位性能的影响从Intel 5300无线网卡中提取信道状态信息（CSI）,利用修正后的CSI幅值和相位信息作为指纹特征,使用极限梯度提升（XGBoost）算法构建高精度指纹库,实现分米级的高精度室内定位。进一步通过实测数据分析了采样间隔、室内视距（LOS）和非视距（NLOS）环境、缺失值和数据维度等因素对所提算法定位性能的影响。实际室内环境下的实验结果表明,本文算法受NLOS影响较小,对室内复杂环境有很强的鲁棒性;此外,该算法能够很好地处理高维稀疏数据,解决CSI指纹特征的"误匹配"问题,且对缺失数据不敏感,定位准确度优于90%。

High-precision WiFi indoor localization algorithm based on CSI-XGBoost

Considering the influence of complex indoor environment and multi-path effects on the WiFi fingerprint positioning performance, this paper extracts channel state information (CSI) from the Intel 5300 wireless network card and utilizes the modified CSI amplitude and phase information as fingerprint features. A high-precision fingerprint database was built using the extreme gradient boosting (XGBoost) algorithm to achieve indoor positioning at a decimeter level. Experiments in the actual indoor environment have been conducted to evaluate the effects of sampling interval,line of sight (LOS) and non line of sight (NLOS), missing values, and data dimensions on the localization performance of the proposed method. The results of real indoor experiment show that the proposed CSI-XGBoost method is less affected by NLOS and robust to complex indoor environments. In addition, this method can handle high-dimensional sparse data well and solve the mismatching problem of CSI fingerprinting. Moreover, this method is insensitive to missing data, with localization accuracy of better than 90%.