Nonparametric cooperative tracking in mobile Ad-Hoc networks

This paper presents a new nonparametric approach for cooperative localisation and tracking by fusing relative range measurements between mobile network equipped nodes. Standard approaches based on parametric methods are known to be limited to problems that contain Gaussian properties. This paper overcomes this limitation by proposing a novel particle filter based cooperative tracking approach that is suitable for mobile ad-hoc networks (MANETs). The filter maintains the marginal state of every node instead of a joint state of the group, and updates the estimates using a Gibbs sampler, which is known as a Markov chain Monte Carlo (MCMC) method. The performance of the proposed algorithm is demonstrated by examining 16 mobile nodes moving randomly while sharing information with neighbouring nodes in a MANET. The results show that the proposed approach facilitates the tracking of mobile nodes that do not have egocentric position information available. It also outperforms the EKF in systems with non-Gaussian properties.