Multi-sensor multiple extended objects tracking based on the message passing

Multiple extended objects (EOs) tracking has attracted a lot of attention due to the fast development of high-resolution sensors. A remarkable feature of EO, compared to the traditional point target, is that an EO normally produces more than one measurement, resulting in challenges for finding the associations among objects and measurements. In this paper, we are interested in tracking an unknown number of EOs with multiple sensors by resorting to the message passing method. The existence probability and belief of each EO are explicitly estimated, where the belief is modeled by a mixture gamma Gaussian inverse Wishart (GGIW) distribution, so as to jointly estimate the measurement rate (MR), centroid state and extension. The marginal posterior of EOs is approximately by belief, which is obtained by running the loopy sum-product algorithm (LSPA) on a suitably devised factor graph. As a result, the computational load of the proposed algorithm increases linearly with respect to the number of targets, thus admitting the scalability. Simulation experiments are carried out to verify the performance of the proposed method.