A Multi-View Discriminant Learning Approach for Indoor Localization Using Bimodal Features of CSI
This addresses device-free indoor localization for location-based services, presenting an incremental improvement through a novel multi-view method.
The paper tackles indoor localization without requiring devices on subjects by proposing MuDLoc, a multi-view discriminant learning approach using amplitude and phase features from Channel State Information (CSI). Experimental results in two cluttered environments show it achieves high accuracy, outperforming benchmark methods.
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.