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Proceedings Paper

Multiple hypotheses tracking with heavy-tailed noise
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Paper Abstract

The Kalman filter, which is optimal with respect to Gaussian distributed noisy measurements, is commonly used in the Multiple Hypothesis Tracker (MHT) for state update and prediction. It has been shown that when filtering noisy measurements distributed with asymptotic power law tails the Kalman filter underestimates the state error when the tail exponent is less than two and overestimates it when the tail exponent is greater that two. This has severe implications for tracking with the MHT which uses the estimated state error for both gating and probability calculations. This paper investigates the effects of different tail exponent values on the processes of track deletion and creation in the MHT.

Paper Details

Date Published: 25 August 2003
PDF: 9 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.487007
Show Author Affiliations
Scott W. Sims, Univ. of Liverpool (United Kingdom)
Jason F. Ralph, Univ. of Liverpool (United Kingdom)
Moira I. Smith, Waterfall Solutions Ltd. (United Kingdom)
Christopher R. Angell, QinetiQ (United Kingdom)
Peter N. Randall, QinetiQ (United Kingdom)

Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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