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• W3013404017 abstract "This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion. After the update step, the posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a Kullback-Leibler divergence minimisation on an augmented space is performed. The developed filters are computationally lighter alternatives to the trajectory PMBM filters, which provide the closed-form recursion for sets of trajectories with Poisson birth model, and are shown to outperform previous multi-target tracking algorithms." @default.
• W3013404017 created "2020-04-03" @default.
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• W3013404017 date "2020-01-01" @default.
• W3013404017 modified "2023-10-01" @default.
• W3013404017 title "Trajectory Poisson Multi-Bernoulli Filters" @default.
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• W3013404017 doi "https://doi.org/10.1109/tsp.2020.3017046" @default.
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