1998
DOI: 10.1117/12.324629
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<title>IMM/MHT solution to radar multisensor benchmark tracking problems</title>

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Cited by 5 publications
(2 citation statements)
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“…For r = 1, the previous linear-Gaussian model results as a limiting case. Fortunately, the tracking performance does not seem to critically depend on the particular choice of the model transition probabilities p(j k │j k-1 ), provided the number r of models involved is small [7]. Let us assume that the previous posterior is written as a Gaussian mixture,…”
Section: ) Gauss-markov Dynamics: a Gauss-markov Dynamics Defined Bmentioning
confidence: 99%
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“…For r = 1, the previous linear-Gaussian model results as a limiting case. Fortunately, the tracking performance does not seem to critically depend on the particular choice of the model transition probabilities p(j k │j k-1 ), provided the number r of models involved is small [7]. Let us assume that the previous posterior is written as a Gaussian mixture,…”
Section: ) Gauss-markov Dynamics: a Gauss-markov Dynamics Defined Bmentioning
confidence: 99%
“…In phased-array radar tracking, additional sensor information can be acquired when needed. Before each "radar resource allocation" [7], certain radar parameters must be selected by the tracking system depending on the current lack of information. We here consider the object revisit time t k , the current beam position b k , i.e.…”
Section: A Sensor Modeling For Phased-array Radarmentioning
confidence: 99%