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arXiv · 1312.6215

Sensor management for multi-target tracking via Multi-Bernoulli filtering

Hung Gia Hoang·Ba Tuong Vo
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Abstract

In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli filter is used in conjunction with two different control objectives: maximizing the expected R\'enyi divergence between the predicted and updated densities, and minimizing the expected posterior cardinality variance. Numerical studies are presented in two scenarios where a mobile sensor tracks five moving targets with different levels of observability.

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Hung Gia Hoang, Ba Tuong Vo. 2013-12-21. Sensor management for multi-target tracking via Multi-Bernoulli filtering. https://doi.org/10.1016/j.automatica.2014.02.007

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