Information theoretic approach to robust multi-Bernoulli sensor control
Abstract
A novel sensor control solution is presented, formulated within a Multi-Bernoulli-based multi-target tracking framework. The proposed method is especially designed for the general multi-target tracking case, where no prior knowledge of the clutter distribution or the probability of detection profile are available. In an information theoretic approach, our method makes use of R\`{e}nyi divergence as the reward function to be maximized for finding the optimal sensor control command at each step. We devise a Monte Carlo sampling method for computation of the reward. Simulation results demonstrate successful performance of the proposed method in a challenging scenario involving five targets maneuvering in a relatively uncertain space with unknown distance-dependent clutter rate and probability of detection.
Cite
@article{arxiv.1502.01066,
title = {Information theoretic approach to robust multi-Bernoulli sensor control},
author = {Amirali K. Gostar and Reza Hoseinnezhad and Alireza Bab-Hadiashar},
journal= {arXiv preprint arXiv:1502.01066},
year = {2015}
}