  With the growth of location-based services, indoor localization is attracting
great interests as it 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 APs. 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 (GI$^{2}$DCA), a discriminative
feature extraction approach using multi-view CSIs. It incorporates inter-view
and intra-view class associations while maximizing pairwise correlations across
multi-view data sets. A similarity measure is performed to find the best match
to localize a subject. Experimental results from two cluttered environments
show that MuDLoc can estimate location with high accuracy which outperforms
other benchmark approaches.
