  Due to the growing area of ubiquitous mobile applications, indoor
localization of smartphones has become an interesting research topic. Most of
the current indoor localization systems rely on intensive site survey to
achieve high accuracy. In this work, we propose an efficient smartphones indoor
localization system that is able to reduce the site survey effort while still
achieving high localization accuracy. Our system is built by fusing a variety
of signals, such as Wi-Fi received signal strength indicator, magnetic field
and floor plan information in an enhanced particle filter. To achieve high and
stable performance, we first apply discriminative learning models to integrate
Wi-Fi and magnetic field readings to achieve room level landmark detection.
Further, we integrate landmark detection, range-based localization models, with
a graph-based discretized system state representation. Because our approach
requires only discriminative learning-based room level landmark detections, the
time spent in the learning phase is significantly reduced compared to
traditional Wi-Fi fingerprinting or landmark-based approaches. We conduct
experimental studies to evaluate our system in an office-like indoor
environment. Experiment results show that our system can significantly reduce
the learning efforts, and the localization method can achieve performance with
an average localization error of 1.55 meters.
