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Mutual Information–based Tracking of Multiple Moving Targets using Networked Robots
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모바일 네트워크를 이용한 상호정보량 기반의 다수 이동 물체 추적 알고리즘 설계

논문 기본 정보

Type
Academic journal
Author
Jinhong Lim (서울대학교) H.Jin Kim (서울대학교)
Journal
Institute of Control, Robotics and Systems Journal of Institute of Control, Robotics and Systems Vol.23 No.3 KCI Accredited Journals SCOPUS
Published
2017.3
Pages
165 - 171 (7page)
DOI
10.5302/J.ICROS.2017.16.0205

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Mutual Information–based Tracking of Multiple Moving Targets using Networked Robots
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Abstract· Keywords

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Tracking multiple moving targets involves many issues such as the sensors’ limited field of view, and the unknown number of targets with unknown dynamics. This paper performs multi-target tracking and target number estimation using a Gaussian mixture probability hypothesis density (GM-PHD) filter. Mutual information is calculated by approximate computation in nonparametric methods and the network of sensing robots is controlled to detect the maximum number of targets by maximizing the mutual information. In addition, we propose the motion pattern learning method using multiple Gaussian Process (GP) models to enhance the multi-target tracking performance for various types of movement by accurately predicting future target states. Among the multiple motion patterns learned in advance, the most proper pattern is assigned by the maximum likelihood principle. The performance of the proposed algorithm is validated via simulation in terms of the accuracy of target number estimation, and the reliability of multi-target tracking.
#motion pattern learning #Gaussian process #GM-PHD filter #mutual information #active sensing

Contents

Abstract
I. 서론
II. 거동패턴 학습 및 물체 위치 예측
III. 다수 이동 물체 위치 추정
IV. 상호정보량 기반의 무인 로봇 의사 결정
V. 시뮬레이션 결과
VI. 결론
REFERENCES

References (24)

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B. Charrow, N. Michael, and V. Kumar, “Cooperative multirobot estimation and control for radio source localization,” The International Journal of Robotics Research, 0278364913500542, 2013. P. Dames and V. Kumar, “Autonomous localization of an unknown number of targets without data association using teams of mobile sensors,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 3, pp. 850-864, 2015. P. Dames, P. Tokekar, and V. Kumar, “Detecting, localizing, and tracking an unknown number of moving targets using a team of mobile robots,” International Symposium of Robotics Research (ISRR), 2015. R. Mahler, “Objective functions for Bayesian control-theoretic sensor management, 1: Multitarget first-moment approximation,” Proc. IEEE Aerospace Conference, vol. 4, 2003. B. Yang and R. Nevatia, “Multi-target tracking by online learning of non-linear motion patterns and robust appearance models,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

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