Variational message passing‐based localisation algorithm with Taylor expansion for wireless sensor networks

Abstract

For localisation algorithms of wireless sensor networks (WSNs), the communication overhead and the computational complexity are two main bottlenecks that should be considered beside the positioning accuracy. In this study, the authors focus on cooperative localisation in WSNs and propose a low-complexity distributed cooperative localisation algorithm by employing variational message passing (VMP) on factor graphs. In order to decrease the communication overhead, Gaussian parametric message representation is adopted. With regard to the non-Gaussian messages caused by the non-linear ranging model, they approximate them to Gaussian messages by exploiting second-order Taylor expansion to reduce the computational complexity. Simulation results show that the proposed algorithm performs quite similar to sum-product algorithm over a wireless network and Gaussian VMP algorithm based on minimising Kullback–Leibler divergence with lower computational complexity.