Passive Indoor Localization Based on CSI and Naive Bayes Classification

Abstract

Passive indoor localization is important. Unlike active localization techniques, it does not require for users to carry measuring devices, e.g., smart phones. Thus, it is widely used in applications such as security, smart housing, object tracking, etc. However, in real-world applications, the passive localization accuracy is limited due to the environment noises, multipath effect, etc. To address those problems, in this paper, we propose to use channel state information (CSI) instead. Specifically, we make the following contributions: 1) we design a CSI-based passive indoor localization system; 2) we develop a Naive Bayes classifier enhanced with confidence level information; and 3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve more than 86% accuracy on average and at least 15% better than the baseline Naive Bayes classifier.