Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback−Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.