Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide & Conquer Method Using Region Allocation Trees

Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide & Conquer Method Using... This paper presents a concurrent region decomposition and allocation algorithm that solves the multi-MSA coordination problem within the context of multi-target tracking and surveillance missions. Our collaboration approach achieves favorable computational characteristics, compared to its alternatives, by taking advantage of a data structure we named region allocation tree and the recursive processing strategy it allows. The region allocation tree data structure identifies the candidate regions, organizes information pertaining to tracking uncertainties and mobile sensor agent assignments, and allows for region decomposition and allocation simultaneously in a single depth-first sweep. Our collaboration approach, here, is used in conjunction with a Bayesian tracking algorithm–as the decision making is carried out in the belief space. Our contributions are also located within the wider discourse on multi-robot coordination. The simulation results demonstrate the effectiveness of our multi-MSA coordination approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent & Robotic Systems Springer Journals

Collaborative Multi-MSA Multi-Target Tracking and Surveillance: a Divide & Conquer Method Using Region Allocation Trees

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media Dordrecht
Subject
Engineering; Control, Robotics, Mechatronics; Electrical Engineering; Artificial Intelligence (incl. Robotics); Mechanical Engineering
ISSN
0921-0296
eISSN
1573-0409
D.O.I.
10.1007/s10846-017-0499-4
Publisher site
See Article on Publisher Site

Abstract

This paper presents a concurrent region decomposition and allocation algorithm that solves the multi-MSA coordination problem within the context of multi-target tracking and surveillance missions. Our collaboration approach achieves favorable computational characteristics, compared to its alternatives, by taking advantage of a data structure we named region allocation tree and the recursive processing strategy it allows. The region allocation tree data structure identifies the candidate regions, organizes information pertaining to tracking uncertainties and mobile sensor agent assignments, and allows for region decomposition and allocation simultaneously in a single depth-first sweep. Our collaboration approach, here, is used in conjunction with a Bayesian tracking algorithm–as the decision making is carried out in the belief space. Our contributions are also located within the wider discourse on multi-robot coordination. The simulation results demonstrate the effectiveness of our multi-MSA coordination approach.

Journal

Journal of Intelligent & Robotic SystemsSpringer Journals

Published: May 4, 2017

References

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