一种基于信道状态信息的无源室内指纹定位算法

A Passive Indoor Fingerprint Localization Algorithm Based on Channel State Information

传统的基于接收信号强度指示的室内定位方法定位精度低、稳定性差。为此,提出一种无源室内定位算法。在商业WiFi设备上采集信道状态信息信号,利用信道中的相应子载波幅度特性进行定位,以有效减轻多径效应。在离线阶段,使用主成分分析法去除噪声、提取特征并建立特征指纹库。在在线阶段,使用朴素贝叶斯分类器实时处理数据,从而得到估计位置。实验结果表明,与DeepFi算法、RSSI算法和FIFS算法相比,该算法具有处理时间短、定位精度高的优点。

Aiming at the low localization accuracy and poor stability of the traditional indoor localization method based on Received Signal Strength Indication（ RSSI）,this paper presents a passive indoor localization method. It collects Channel State Information（ CSI） signals on commercial WiFi devices. The corresponding sub carrier amplitude characteristics are used in the channel to locate,in order to effectively mitigate the multipath effect. In the offline phase,Principal Component Analysis（ PCA） method is utilized to remove the noise, extract the features and establish the feature fingerprints. In the online phase,the Naive Bayesian Classifier（ NBC） method is used to process the data in real time and then get the estimated position. Experimental results show that the proposed algorithm has the advantages of short processing time and high localization accuracy compared with DeepFi algorithm,RSSI algorithm and FIFS algorithm.