基于CSI信号的被动式室内指纹定位算法研究

基于信道状态信息(CSI)的定位技术在室内场景应用中被广泛关注，为了提高WiFi信号多径效应对接收信号强度指示的室内定位精度和稳定性，提出一种基于CSI信号的被动式室内指纹定位算法。该算法在离线阶段将定位场所划分为同等大小的区域块，在各连接点位置使用方差补偿的自适应卡尔曼滤波（Kalman）算法对原始数据进行滤波。再对滤波后的数据使用二分K均值聚类（K-means）算法进行分类，将处理得到的CSI幅值和相位信息共同作为指纹；在线阶段根据待测点采集的实时数据与指纹库进行匹配识别，被定位对象无需携带任何设备。仿真实验与实地实验表明，该算法利用信道状态信息中的子载波特征进行定位，能够有效减轻信号接收端的多径衰减影响，定位精度有明显提高。 

A passive indoor fingerprint positioning algorithm based on CSI signal

The positioning technology based on channel state information (CSI) has been widely concerned in indoor scene applications. In order to improve the indoor positioning accuracy and stability of the WiFi signal multipath effect on the received signal strength indicator, this paper proposes a passive indoor fingerprint positioning algorithm based on CSI signal. In the offline phase, the location is divided into blocks of the same size. The original data are filtered by the adaptive Kalman filter algorithm with variance compensation at each connection point, and then the filtered data are classified by the bisecting K-means clustering algorithm. The amplitude and phase information of the processed CSI are used as fingerprints. In the online stage, the real-time data collected by the test points are matched with the fingerprint database, and the located target does not need to carry any equipment. The simulation and field experiments show that the proposed algorithm can effectively reduce the multi-path attenuation effect of the signal receiver by using the sub-carrier characteristics in CSI signals, the positioning accuracy is significantly improved.