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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Vydané v:2004 American Control Conference Proceedings; Volume 5 of 6 Ročník 5; s. 4750 - 4751 vol.5
Hlavní autori: Mallick, M., Pao, L.Y., Chang, K.C.
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway NJ IEEE 01.01.2004
Evanston IL American Automatic Control Council
Predmet:
Additive noise
Applied sciences
Computational complexity
Computer architecture
Computer errors
Computer science; control theory; systems
Control theory. Systems
Covariance matrix
Decorrelation
Distributed computing
Exact sciences and technology
Measurement errors
Modelling and identification
State estimation
Target tracking
Target tracking
Probabilistic approach
Clutter
Distributed algorithm
ISBN:9780780383357, 0780383354
ISSN:0743-1619
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Abstract 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.
AbstractList 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.
Author Pao, L.Y.
Mallick, M.
Chang, K.C.
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Keywords Target tracking
Probabilistic approach
Clutter
Distributed algorithm
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SubjectTerms Additive noise
Applied sciences
Computational complexity
Computer architecture
Computer errors
Computer science; control theory; systems
Control theory. Systems
Covariance matrix
Decorrelation
Distributed computing
Exact sciences and technology
Measurement errors
Modelling and identification
State estimation
Target tracking
Title Multiple hypotheses tracking based distributed fusion using decorrelated pseudo measurement sequence
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