Evaluating Indoor Localization Performance on an IEEE 802.11ac Explicit-Feedback-Based CSI Learning System

There is a demand for device-free user location estimation with high accuracy in order to realize various indoor applications. This paper proposes an IEEE 802.11ac explicit feedback-based channel state information (CSI) learning system which can be used for device-free user location estimation. The proposed CSI learning system captures CSI feedback from off-the-shelf Wi-Fi devices and extracts 624 features from a CSI feedback frame defined in IEEE 802.11ac. We evaluated the proposed system using location estimation with six patterns: different combinations of device-free user movement and access point antenna orientation. The evaluation results show that the machine learning based localization achieves approximately 96$\%$ accuracy for seven positions of the user, and the divergence of CSI improves localization performance.