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A Risk-Based Sensor Management using Random Finite Sets and POMDP

Abstract : In this paper, we consider the problem of scheduling an agile sensor to perform an optimal control action in the case of the multi-target tracking scenario. Our purpose is to present a random finite set (RFS) approach to the multi- target sensor management problem formulated in the Partially Observed Markov Decision Process (POMDP) framework. The reward function associated with each sensor control (action) is computed via the Expected Risk Reduction between the multi- target predicted and updated densities. The proposed algorithm is implemented via the Probability Hypothesis Density filter (PHD). Numerical studies demonstrate the performance of this particular approach to a radar beam-pointing problem where targets need to be prioritized.
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https://hal.archives-ouvertes.fr/hal-02411689
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Submitted on : Sunday, December 15, 2019 - 10:26:14 AM
Last modification on : Friday, December 11, 2020 - 6:44:05 PM
Long-term archiving on: : Monday, March 16, 2020 - 2:20:44 PM

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Marcos Gomes Borges, Dominique Maltese, Philippe Vanheeghe, Emmanuel Duflos. A Risk-Based Sensor Management using Random Finite Sets and POMDP. 20th International Conference on Information Fusion (FUSION), Jul 2017, Xi'an, China. pp.1-9, ⟨10.23919/ICIF.2017.8009843⟩. ⟨hal-02411689⟩

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