Smartphone positioning in sparse Wi-Fi environments

Indoor localization using mobile devices such as smartphones remains a 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. 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. We test our approach in two real-world environments to show the benefits of incorporating user motion for indoor localization.