A Multi-View Discriminant Learning Approach for Indoor Localization Using Amplitude and Phase Features of CSI

Abstract

Location Based Service (LBS) is one of the important aspects of a smart city. Accurate indoor localization plays a vital role in LBS. The ability to localize various subjects in the area of interest facilitates further ubiquitous environments. Specifically, device free localization using wireless signals is getting increased attention as human location is estimated using its impact on the surrounding wireless signals without any active device tagged with subject. In this paper, we propose MuDLoc, the first multi-view discriminant learning approach for device free indoor localization using both amplitude and phase features of Channel State Information (CSI) from multiple Access Points (APs). The same location oriented CSI data can be observed by different APs, thus generating multiple distinct even heterogeneous samples. Multi-view learning is an emerging technique in machine learning which improve performance by utilizing diversity from different view data. In MuDLoc, the localization is modeled as a pattern matching problem, where the target location is predicted based on similarity measure of CSI features of an unknown location with those of the training locations. MuDLoc implements Generalized Inter-view and Intra-view Discriminant Correlation Analysis (GI2DCA), a discriminative feature extraction approach that incorporates inter-view and intra-view class associations while maximizing pairwise correlations across multi-view data sets. Experimental results from two cluttered environments show that MuDLoc can estimate location with high accuracy which outperforms other benchmark approaches.