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This paper introduces a general chain rule (GCR) for Gâteaux differentials/Gâteaux derivatives, and describes its consequences for multitarget detection and tracking. After describing the GCR and its specific form for functionals and functional derivatives, we use it to derive two new PHD filters: (1) a PHD filter for general models of target-generated measurements with general clutter processes;...
The context is sensor control for multi-object Bayes filtering in the framework of partially observed Markov decision processes (POMDPs). The current information state is represented by the multi-object probability density function (pdf), while the reward function associated with each sensor control (action) is the information gain measured by the alpha or Rényi divergence. Assuming that both the...
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