TrueStory: Accurate and Robust RF-Based Floor Estimation for Challenging Indoor Environments

摘要

WiFi-based indoor localization systems are popular due to the WiFi ubiquity and availability in commodity smartphone devices for network communication. The majority of these systems focus on finding the user's 2-D location in a single floor. However, this is of little value when the altitude of the user is unknown in any typical multi-story building. In this paper, we propose TrueStory: a system that can accurately and robustly identify the user's floor level using the building's WiFi networks. TrueStory targets challenging environments where the access point (AP) density is not uniform and/or there are open areas that make the APs heard strongly in faraway floors. To handle these challenges, TrueStory employs a number of techniques including signal normalization, AP power equalization, and fusing various learners using a multilayer perceptron neural network. We present the design and implementation of TrueStory and evaluate its performance in three different testbeds. Our evaluation shows that TrueStory can accurately identify the user's exact floor level up to 91.8% of the time and within one floor error 99% of the time. This improves the floor estimation accuracy over the state-of-the-art systems and reduces the high floor errors by more than 23%. In addition, we show that it has a robust performance for various challenging environments.