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Over-the-Horizon Radar Network Measurement-Level Fusion Based on Message Passing Approach

This paper considers the problem of tracking an unknown number of targets based on multipath measurements provided by an over-the-horizon radar (OTHR) network with a statistical ionospheric model, which suffers from the intractable inference of high-dimensional latent variables including target kinematic state, target visibility state, multipath data association, and ionospheric height. A unified message passing approach, combining belief propagation (BP) and mean-field (MF) approximation, is developed for simplifying the intractable inference. Based upon the factor graph corresponding to a factorization of the joint probability distribution function (PDF) of the latent variables and a choice for a separation of this factorization into BP region and MF region, the posterior PDFs of continuous latent variables including target kinematic state, target visibility state, and ionospheric height, are approximated by MF due to its simple MP update rules for conjugate-exponential models. With regard to discrete multipath data association which contains one-to-one frame (hard) constraints, its probability mass function (PMF) is approximated by loopy BP. Finally, the approximated posterior PDFs and PMF are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue among target state estimation, multipath data association, and ionospheric height identification. Meanwhile, the proposed approach has the measurement-level fusion architecture due to the direct processing of the raw multipath measurements from an OTHR network, which is benefit to improving target tracking performance. Its performance is demonstrated on a simulated OTHR network multitarget tracking scenario.