Multiple hypotheses tracking based distributed fusion using decorrelated pseudo measurement sequence

A joint probabilistic data association based algorithm for multi-target tracking in clutter using the distributed tracking architecture has been proposed recently. The algorithm uses the decorrelated state estimates or equivalent pseudo measurements. This paper extends the previous approach to the m...

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Bibliographic Details
Published in:2004 American Control Conference Proceedings; Volume 5 of 6 Vol. 5; pp. 4750 - 4751 vol.5
Main Authors: Mallick, M., Pao, L.Y., Chang, K.C.
Format: Conference Proceeding Journal Article
Language:English
Published: Piscataway NJ IEEE 01.01.2004
Evanston IL American Automatic Control Council
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ISBN:9780780383357, 0780383354
ISSN:0743-1619
Online Access:Get full text
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Summary:A joint probabilistic data association based algorithm for multi-target tracking in clutter using the distributed tracking architecture has been proposed recently. The algorithm uses the decorrelated state estimates or equivalent pseudo measurements. This paper extends the previous approach to the multi-target tracking problem in clutter with probability of detection less than unity using the track-oriented multiple hypotheses tracking framework. We present multiple hypotheses distributed tracking algorithms for track initialization, gating, hypothesis generation, track update, computation of track likelihood, formation of global hypothesis, and pruning using the pseudo measurement formulation.
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ISBN:9780780383357
0780383354
ISSN:0743-1619
DOI:10.23919/ACC.2004.1384062