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JPDAS Multi-Target Tracking Algorithm for Cluster Bombs Tracking
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 Title & Authors
JPDAS Multi-Target Tracking Algorithm for Cluster Bombs Tracking
Kim, Hyoung-Rae; Chun, Joo-Hwan; Ryu, Chung-Ho; Yoo, Seung-Oh;
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 Abstract
JPDAF is a method of updating target`s state estimation by using posterior probability that measurements are originated from existing target in multi-target tracking. In this paper, we propose a multi-target tracking algorithm for falling cluster bombs separated from a mother bomb based on JPDAS method which is obtained by applying fixed-interval smoothing technique to JPDAF. The performance of JPDAF and JPDAS multi-target tracking algorithm is compared by observing the average of the difference between targets` state estimations obtained from 100 independent executions of two algorithms and targets` true states. Based on this, results of simulations for a radar tracking problem that show proposed JPDAS has better tracking performance than JPDAF is presented.
 Keywords
Tracking;JPDAF(Joint Probabilistic Data Association Filter);JPDAS(Joint Probabilistic Data Association Smoothing);Fixed-Interval Smoothing;
 Language
Korean
 Cited by
 References
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