Gaussian message passing for cooperative localization in wireless networks

Abstract

Cooperative localization has become a novel technique to improve the performance in harsh environment with insufficient anchors. In this paper, distributed cooperative localization is studied based on message passing on factor graph. The factor graph is constructed according to the joint a posteriori distribution of nodes' positions. Because of the nonlinearity between the positions and observations, the updates of messages using the sum-product algorithm cannot obtain closed forms. To tackle the nonlinearity, the linearization of range measurement based on its own and the neighbors' positions is utilized. Accordingly, we are able to derive all the messages on factor graph and the nodes' position posteriors as Gaussian distributions, which only requires to update the mean vectors and covariance matrices. Numerical results are performed for both static and mobile networks. The performance of the proposed algorithm reaches better performance than the maximum likelihood estimator, SPAWN, and extended Kalman filter. Besides, the proposed algorithm holds the low communication overhead by transmitting the parametric messages between the nodes.