• Title/Summary/Keyword: JPDAF(Joint Probabilistic Data Association Filter)

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JPDAS Multi-Target Tracking Algorithm for Cluster Bombs Tracking (자탄 추적을 위한 JPDAS 다중표적 추적알고리즘)

  • Kim, Hyoung-Rae;Chun, Joo-Hwan;Ryu, Chung-Ho;Yoo, Seung-Oh
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.27 no.6
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    • pp.545-556
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    • 2016
  • 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.

Track initiation for joint probabilistic data association filter (결합확률 데이타 연관 필터에서의 표적 초기화)

  • 김학용;박용환;황익호;서진헌
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.141-146
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    • 1992
  • Joint probabilistic data association filter(JPDAF) for multi-target tracking was developed for real-time implementation, while it abandoned an algorithm for track initiation. In this paper, we propose three features for track initiation that can be adapted to the JPDA filter. In addition, with the proposed approaches, the performance of track maintenance is evaluated in the case of tracks being near. To eliminate the abundant false tracks, we exploit the simple method using the state error covariances. Simulations are performed to demonstrate the efficiency of the proposed approaches.

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