基于CSI和K-means-SVR的多指纹库室内定位方法

Indoor positioning method of multi-fingerprint database based on channel state information and K-means-SVR

传统的Wi-Fi室内定位方法需要与所有指纹数据库中的指纹数据进行匹配后才能定位,导致人群聚集区域定位效率不高,体验较差。提出一种基于信道状态信息(CSI)、K均值(K-means)聚类算法与支持向量回归(SVR)算法相结合的多指纹库室内定位方法。该方法首先根据CSI的簇分布特点,利用K-means算法对所有定位点内的CSI数据聚类后得到多个簇的CSI数据;然后,基于多个簇分别建立多个指纹库,并将CSI数据分别存入多个指纹库,进而在每个指纹库中分别训练SVR模型用于Wi-Fi定位。相较于传统的支持向量机(SVM)定位方法,所提方法在离线阶段需要的训练样本更少,定位效率更高;在线阶段,该方法既降低了匹配的复杂度,也提高了定位的精度。由于使用了多指纹库,Wi-Fi定位系统可以根据人流量实时调整资源分配策略,提高服务器运行效率和定位服务体验。

The traditional Wi-Fi indoor positioning methods need to match all fingerprint data in the fingerprint database before positioning,resulting in low positioning efficiency and poor experience in the crowd gathering area.Therefore,a multi-fingerprint database indoor positioning method based on Channel State Information(CSI),K-means clustering algorithm and Support Vector Regression(SVR)algorithm was proposed.Firstly,according to the cluster distribution characteristics of CSI,K-means algorithm was used to cluster the CSI data in all positioning points to obtain the CSI data of multiple clusters.Then,multiple fingerprint databases were established based on multiple clusters,and the CSI data was stored in multiple fingerprint databases.After that,SVR models were trained in each fingerprint database for Wi-Fi positioning.Compared with the traditional Support Vector Machine(SVM)positioning method,the proposed method needs less training samples in the off-line stage,which improves the positioning efficiency;in the online stage,this method not only reduces the matching complexity,but also improves the positioning accuracy.Due to the use of multi-fingerprint database,the Wi-Fi positioning system can adjust the resource allocation strategy in real time according to the traffic,so as to improve the server operation efficiency and positioning service experience.