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Particle approximations of a class of branching distribution flows arising in multi-target tracking

François Caron 1, 2 Pierre del Moral 1, 2 Arnaud Doucet 3 Michele Pace 1
1 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : We design a mean field and interacting particle interpretation of a class of spatial branching intensity models with spontaneous births arising in multiple-target tracking problems. In contrast to traditional Feynman-Kac type particle models, the transitions of these interacting particle systems depend on the current particle approximation of the total mass process. In the first part, we analyze the stability properties and the long time behavior of these spatial branching intensity distribution flows. We study the asymptotic behavior of total mass processes and we provide a series of weak Lipschitz type functional contraction inequalities. In the second part, we study the convergence of the mean field particle approximations of these models. Under some appropriate stability conditions on the exploration transitions, we derive uniform and non asymptotic estimates as well as a sub-gaussian concentration inequality and a functional central limit theorem. The stability analysis and the uniform estimates presented in the present article seem to be the first results of this type for this class of spatial branching models.
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François Caron, Pierre del Moral, Arnaud Doucet, Michele Pace. Particle approximations of a class of branching distribution flows arising in multi-target tracking. SIAM Journal on Control and Optimization, Society for Industrial and Applied Mathematics, 2011, 49 (4), pp.1766-1792. ⟨10.1137/100788987⟩. ⟨inria-00464130⟩

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