Accelerating crowdsourcing based indoor localization using CSI

Indoor localization is of importance for many applications. Crowdsourcing individual users' measurements can provide accurate localization without costly site-survey. However, crowdsourcing based approaches suffer from the cold start problem, in which at the beginning of system deployment, there are insufficient users to contribute their measurements, resulting in inaccurate and time-inefficient localization. In this paper, we propose a hybrid indoor localization method to solve such problem, called ACIL. We first employ the inertial navigation technique to localize some core positions or paths. To tackle the inaccuracy problem, we propose an effective method that utilizes the channel state information (CSI) of wireless signals for accurate distance estimation. This method is based on a new observation: there is a ripple-like fading pattern in wireless signals upon moving objects. Leveraging this observation, our system is capable of calculating the distance of human's movement and his/her direction. We also propose a graph-matching algorithm to setup the correlation between the trajectory and floor map. With those extra obtained location information, the impact of cold start issue will be significantly mitigated, while the LBS can be guaranteed with high localization accuracy. Extensive experiments show that the effectiveness in the human localization and movement detection. Extensive experiments validate the great performance of our protocol in case of various human locations and diverse channel conditions.