Measuring and Modeling Multipath of Wi-Fi to Locate People in Indoor Environments

With the rapid development of the Internet of Things (IoT) technology, the position information of indoor people has become an indispensable factor in most fields. Most existing indoor positioning schemes require people to keep moving to detect significant variance of the signal as the location feature. Hence, this paper proposes a passive indoor positioning system based on commodity Wi-Fi called Wisite, which can implement indoor multipath signal measurement and static person positioning modeling. The biggest challenge is how to detect the dynamic features in the reflection path of the static person to achieve target path matching. To address this issue, Wisite proposes a MUSIC expectation-maximization (MEM) joint parameter estimation algorithm to estimate and enhance the indoor multipath parameters. Then, a dynamic path matching model based on signal change enhancement (SCE) is proposed to enhance the signal changes caused by human activities, which can amplify the weak signal changes introduced by human respiration when a person is in a static state. Finally, the multipath geometric positioning model is used to calculate the person's position. We implement Wisite using commercial off-the-shelf (COTS) IEEE 802.11n devices and evaluate its performance via extensive experiments in typical real-world scenes. The results show that Wisite outperforms the comparison approaches in estimating accuracy and effectiveness with the average indoor positioning error is less than 0.65cm.