Joint synchronization and localization based on Gaussian belief propagation in sensor networks

In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.