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 Interview and Intra-view Discriminant Correlation Analysis (GI 2 DCA), a discriminative feature extraction approach that incorporates interview 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.

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

“…Through a review of the recent literature, we find that, to accurately locate targets, the previous studies have proposed many schemes, such as deep neural networks (DNNs) [18], KNN [11], radio tomographic imaging (RTI) [19], etc., using the RSS or the channel state information (CSI) signal [20]- [22]. Subsequently, to further improve the localization accuracy and performance, many studies exploited the sparserepresentation model of signals for DFL [15]- [17].…”

Machine Learning for Practical Localization System Using Multiview CSI

“…Support vector regression (SVR) based system was introduced in [24] for device-free localization which [25], [26] also considered. There are other various learning techniques for localization, such as autoencoder [27], clustermapping (C-Map) [28], kNN [29], multi-layer perceptron (MLP), [30], canonical correlation analysis (CCA) [31], and visibility graph (VG) [32].…”

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

“…The C-Map proposed by WEN et al [ 18 ] achieves an average positioning error in the comprehensive environment of 0.97 m. CHAUR-HEH et al [ 19 ] adopted a convolutional neural network to localization in a room, which performs the best, resulting in a maximal localization error of 0.92 m with a probability of 99.97%. YUAN et al In [ 20 ], a multi-view discriminant learning approach was developed for indoor localization that exploits both the amplitude and the phase information of CSI to create feature images for each location, and the minimum distance errors for the laboratory and corridor experiments were 0.205 m and 0.109 m, respectively. In [ 21 ], the authors proposed to transform the measured data from CSI into images, extract their features, and use deep learning networks to estimate localization, and achieved an accuracy of more than 90% in laboratories.…”