Multiview Features Fusion and AdaBoost Based Indoor Localization on WiFi Platform
Jianyang Ding, Yong Wang, Shaozhong Fu, Hongyan Si, Jing Zhang, Shang Gao
Abstract
With the increasing demand for location-based service (LBS), WiFi-based localization has attracted considerable attentions due to the wide deployment and low cost of WiFi devices. However, most of existing approaches need to work within line-of-sight (LoS) range or require users to take dedicated devices. Thus, a device-free and passive indoor localization scheme is still desired. In this paper, a multi-view features fusion and AdaBoost-based indoor localization scheme by exploiting channel state information (CSI) of WiFi signals is proposed. To this end, we firstly formulate a shared consistent representation by using CSI measurements from all WiFi receivers, modeling the common properties among all views. Based on this, a features extraction is applied to the consistent representation for charactering channel properties. Meanwhile, these features extracted are optimized via a t-distributed stochastic neighbor embedding (t-SNE) algorithm for selecting the effective features, contributing to system performance. AdaBoost then builds a non-linear mapping between the CSI features and locations, realizing the purpose of localizing a user relying solely on WiFi signals with a high level of granularity, without any active engagement from the users. We implement a prototype on commodity WiFi devices and conduct comprehensive experiments in indoor scenarios. Based on real-world CSI measurements, the results confirm that the proposed scheme can achieve average localization errors of 0.8 m and 1.1 m in two indoor scenarios.