I. Introduction
Indoor positioning has received great attention recently because position information is essential for providing location-based services (LBSs) [1], which offers intelligent services in various fields in the context of Internet of Things (IoT) [2]–[4]. For example, position-based navigation has been used inside Copenhagen Airport [5]. Passengers there can use it to plan their paths inside the airport and to get expected information in an interactive way. Moreover, an indoor tracking system was deployed in Hartford hospital, which helps tracking expensive equipment and also assisting patients there to efficiently use medical resources in the hospital [6]. In addition, location-aware advertising usually delivers location-specific coupons or discount information to customers based on their locations and interests [7]. However, indoor environments are very complicated such that there usually exist many obstacles, such as walls, furniture, human beings, and consequently fluctuations of wireless signals because of multipath effects. These obstacles and the signal fluctuations at different scales can cause significant degradation in the accuracy of indoor positioning, which limits the usefulness and degree of comfortableness for providing practical LBS services.