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• W1737669301 abstract "It is one of the difficulties for space-based optical sensors to track a group of closely spaced objects (CSOs). A suitable way is to treat the CSOs as extended targets, tracking the centroid states and extensions jointly instead of individual objects. The target-generated measurements are modeled by the elliptic Random Hypersurface model (RHM) which can be easily embedded into other Bayesian inference algorithms. Here, the RHM is incorporated with the Gaussian-mixture probability hypothesis density (GM-PHD) for extended target tracking, which enables estimating shape-varying targets in presence of clutter and uncertain associations. Compared with the Gaussian inverse Wishart (GIW) PHD, simulation results demonstrate the capabilities and limitations of the proposed method. At last, a ballistic missile defense scene is established to depict how the method is to handle the CSOs tracking issue." @default.
• W1737669301 created "2016-06-24" @default.
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• W1737669301 date "2013-07-09" @default.
• W1737669301 modified "2023-09-27" @default.
• W1737669301 title "A PHD filter for tracking closely spaced objects with elliptic Random Hypersurface models" @default.
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