title: Sensor Fusion of Gaussian Mixtures for Ballistic Target Tracking in the Re-Entry Phase.
abstract: A sensor fusion methodology for the Gaussian mixtures model is proposed for ballistic target tracking with unknown ballistic coefficients. To improve the estimation accuracy, a track-to-track fusion architecture is proposed to fuse tracks provided by the local interacting multiple model filters. During the fusion process, the duplicate information is removed by considering the first order redundant information between the local tracks. With extensive simulations, we show that the proposed algorithm improves the tracking accuracy in ballistic target tracking in the re-entry phase applications.
authors.affiliation: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China. klu@buaa.edu.cn.
authors.forename: Kelin
authors.initials: K
authors.lastname: Lu
authors.affiliation: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China. zhr@buaa.edu.cn.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhou
keywords: Gaussian mixtures; sensor fusion; target tracking