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With the growth of location-based services, indoor localization is attractinggreat interests as it facilitates further ubiquitous environments.Specifically, device free localization using wireless signals is gettingincreased attention as human location is estimated using its impact on thesurrounding wireless signals without any active device tagged with subject. Inthis paper, we propose MuDLoc, the first multi-view discriminant learningapproach for device free indoor localization using both amplitude and phasefeatures of Channel State Information (CSI) from multiple APs. Multi-viewlearning is an emerging technique in machine learning which improve performanceby utilizing diversity from different view data. In MuDLoc, the localization ismodeled as a pattern matching problem, where the target location is predictedbased on similarity measure of CSI features of an unknown location with thoseof the training locations. MuDLoc implements Generalized Inter-view andIntra-view Discriminant Correlation Analysis (GI$^{2}$DCA), a discriminativefeature extraction approach using multi-view CSIs. It incorporates inter-viewand intra-view class associations while maximizing pairwise correlations acrossmulti-view data sets. A similarity measure is performed to find the best matchto localize a subject. Experimental results from two cluttered environmentsshow that MuDLoc can estimate location with high accuracy which outperformsother benchmark approaches.

A Multi-View Discriminant Learning Approach for Indoor Localization Using Bimodal Features of CSI