Sensor control for multi-target tracking using Cauchy-Schwarz divergence

@article{Beard2015SensorCF,
  title={Sensor control for multi-target tracking using Cauchy-Schwarz divergence},
  author={Michael Beard and Ba-Tuong Vo and Ba-Ngu Vo and Sanjeev Arulampalam},
  journal={2015 18th International Conference on Information Fusion (Fusion)},
  year={2015},
  pages={937-944}
}
In this paper, we propose a method for optimal stochastic sensor control, where the goal is to minimise the estimation error in multi-object tracking scenarios. Our approach is based on an information theoretic divergence measure between labelled random finite set densities. The multi-target posteriors are generalised labelled multi-Bernoulli (GLMB) densities, which do not permit closed form solutions for traditional information divergence measures such as Kullback-Leibler or Rényi. However… CONTINUE READING
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