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- aggregation classification "P1".
- aggregation creator B56827.
- aggregation creator B56828.
- aggregation creator B56829.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2009".
- aggregation format "application/pdf".
- aggregation hasFormat 725031.bibtex.
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- aggregation hasFormat 725031.dc.
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- aggregation hasFormat 725031.doc.
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- aggregation isPartOf urn:isbn:9780982443804.
- aggregation language "eng".
- aggregation publisher "IEEE".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Science General".
- aggregation title "Multiple model tracking by imprecise Markov trees".
- aggregation abstract "We present a new procedure for tracking manoeuvring objects by hidden Markov chains. It leads to more reliable modelling of the transitions between hidden states compared to similar approaches proposed within the Bayesian framework: we adopt convex sets of probability mass functions rather than single `precise probability' specifications, in order to provide a more realistic and cautious model of the manoeuvre dynamics. In general, the downside of such increased freedom in the modelling phase is a higher inferential complexity. However, the simple topology of hidden Markov chains allows for efficient tracking of the object through a recently developed belief propagation algorithm. Furthermore, the imprecise specification of the transitions can produce so-called indecision, meaning that more than one model may be suggested by our method as a possible explanation of the target kinematics. In summary, our approach leads to a multiple-model estimator whose performance, investigated through extensive numerical tests, turns out to be more accurate and robust than that of Bayesian ones.".
- aggregation authorList BK144169.
- aggregation endPage "1774".
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- aggregation aggregates 725200.
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