Wimage: Crowd Sensing based Heterogeneous Information Fusion for Indoor Localization

Crowd sensing is an efficient way to collect heterogeneous information in the complicated infrastructures for fingerprinting based indoor localization. However, the information related to the dynamic trajectory are difficult to fuse due to the reliability issues from different devices and user moving habits. In this paper, we proposed a crowd sensing based indoor localization system with heterogeneous information fusion, which is called Wimage. Wimage can efficiently fuse multiple information sources related to location information, e.g., visual image, WiFi and geomagnetic data, even if the targets are moving with different and variable speeds. Then we design image-base subregion matching algorithm to locate the initial position and segmented weighted K-nearest neighbor algorithm to attain the matched trajectories in the database. A dynamic temporal warping algorithm is proposed for further calibrating the estimations. The experimental results indicate that with the helps from different kinds of information, the root mean square error is only below 0. 4m, which is highly accurate for locating a target in a large scale of indoor environment.