Motion assisted indoor smartphone positioning in sparse Wi-Fi environments

The current generation of smartphone devices equipped with embedded sensors like gyroscope, accelerometer and electronic compass, provide new opportunities for user positioning and tracking. In addition, the rapid growth of location based applications has spurred extensive research on localization. However localization in indoor environments still remains an elusive and challenging problem as GPS (Global Positioning System) does not work inside buildings and the accuracy of other localization techniques typically comes at the expense of additional infrastructure or cumbersome war-driving. Specifically, in places where Wi-Fi access points are sparsely deployed, localization becomes more challenging when relying only on Wi-Fi based technologies. For such environments, we propose a localization scheme which uses motion information from the smartphone's accelerometer, magnetometer, and gyroscope sensors to detect steps and estimate direction changes. At the same time, we use a Wi-Fi based fingerprinting technique for independent position estimation. These measurements along with an internal representation of the environment are combined using a Bayesian filter. This system will allow us to reduce the amount of training required and work in sparse Wi-Fi environments