A Bayesian Algorithm for Distributed Network Localization Using Distance and Direction Data

A reliable, accurate, and affordable positioning service is highly required in wireless networks. High-resolution estimates of distance and direction data are available in most current and emerging wireless systems. Combining these two sensing modalities can improve the estimation performance and identifiability of the cooperative localization problem, and reduce its sensitivity to the geometry of anchor nodes, i.e., the reference nodes with known locations. However, this is still an open and challenging research problem. In this paper, the novel Message Passing Hybrid Localization (MPHL) algorithm is proposed to solve the problem of cooperative distributed localization using joint distance and direction estimates without any prior information. A statistical model is formulated for the problem, and approximate minimum mean square error (MMSE) estimates of the node locations are computed. The proposed MPHL algorithm is a distributed technique based on belief propagation and Markov chain Monte Carlo sampling. Numerical results are presented showing that the average localization error is significantly reduced in almost every simulation scenario, about 50% in most cases, compared to the state of the art. This improvement in localization performance is due to close approximation of a statistically optimal MMSE estimator.