摘要： 在现有基于信道状态信息的室内无源定位方法中，取样点的选取对指纹库的特征匹配准确率以及定位精度具有较大影响。根据WiFi信号的传输特性和信道的衰落特征，提出一种30°；角同心圆环形取样法。离线阶段，按照同心圆对检测区域实现环形划分并每隔30°；进行一次取样，运用主成分分析算法提取差异化信号特征并构建指纹库。在线阶段，通过陆地移动距离算法进行入侵检测，当检测到有人存在时，利用改进的支持向量回归算法并引入高斯核函数对数据进行特征匹配，最终实现人员的精确定位。实验结果表明，与CSI-MIMO、FIFS方法相比，该方法定位精度更高，定位误差更小。

关键词: 入侵检测, 室内定位, 30°角同心圆环形取样法, 主成分分析, 支持向量回归

Abstract: In the existing indoor passive location methods based on Channel State Information(CSI),the selection of sampling points has great impact on the feature matching accuracy and location accuracy of fingerprint database.According to the transmission features of WiFi and the fading features of channel,this paper proposes a 30° angle concentric circular sampling.In the offline phase,concentric circles are used to divide the detection area,taking samples every 30° angle.The differential signal features are extracted by the Principal Component Analysis(PCA) and the fingerprint database is constructed.In the online state,the land mobile distance algorithm is used for intrusion detection.When someone is detected,this method uses the improved Support Vector Regression(SVR) algorithm and introduces Gaussian kernel function to match the features of data,thus achieving precise location of the personnel.Experimental results show that compared with CSI-MIMI and FIFS methods,the proposed method has higher positioning accuracy and lower positioning error.

Key words: intrusion detection, indoor positioning, 30° angle concentric circular sampling method, Principal Component Analysis(PCA), Support Vector Regression(SVR)

中图分类号: 

• TP391