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Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach

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Authors
Kim, Du Yong
Date
2018
Type
Conference Paper

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Kim, D.Y. 2018. Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach, pp. 1438-1444.
Source Title
2018 21st International Conference on Information Fusion, FUSION 2018
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School of Electrical Engineering, Computing and Mathematical Science (EECMS)
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Abstract

© 2018 ISIF This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (RHE). The proposed algorithm is based on the generalized labeled multi-Bernoulli (GLMB) filter which enables RHE for multi-target tracking. The proposed algorithm, a Receding Horizon GLMB (RH-GLMB) filter, is evaluated through a numerical example and visual tracking datasets where dynamic modeling uncertainty exists.