LAUSR

With the prevalence of commodity WiFi devices and development of the Internet of Things (IoT), the usage of WiFi has been extended from communication to context aware. Based on this, indoor localization has attracted increasing attention in the academic community, without the need for additional sensors and any active engagement from the users. However, the localization performance is vulnerable to the background noises due to relying on signals changes. To address this issue, in this article, we propose a passive 3-D indoor localization with a radio map for the mobile target by exploiting channel state information (CSI) of WiFi signals, realizing human–computer interaction (HCI). To this end, 3-D space is first divided into multiple independent regions and we construct a spatial radio map by traversing all the subspaces using CSI measurements. Next, to fully characterize the profiles of the locations of the mobile target, we reconstruct CSI time series to form a CSI tensor through integrating WiFi transmission links and a CANDECAMP/PARAFAC (CP) decomposition method is applied to this tensor for obtaining representative features. Then, the features-location data set is optimized by combining $t$ -distributed stochastic neighbor embedding (t-SNE) and information theory method to reconstruct a fine-grained fingerprint map for improving system performance. Finally, a recurrent neural network (RNN) model is introduced to learn the features data set optimized and then build a nonlinear correlation between input and output for realizing the purpose of accurate indoor localization. The proposed scheme is implemented on a set of commodity WiFi devices and evaluated in indoor scenarios. Based on real-world CSI data, our experimental results confirm the effectiveness of the proposed scheme in terms of localization accuracy and robustness against the noises.