Cooperative indoor positioning by exchange of bluetooth signals and state estimates between users

This paper presents a Bayesian indoor positioning system for smartphones based on the strengths of WiFi and Bluetooth signals. A framework for improving the performance of existing positioning methods with the help information sharing between users is proposed and evaluated. Bluetooth signals are sent between users, and the signal strengths contain information about their relative distances, which is used to evaluate the probability distribution functions of their states. A particle filter is used for the state estimation, together with an unscented transform to propagate probability distributions through nonlinearities.