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Proceedings Paper

Maximum likelihood method for probabilistic multihypothesis tracking
Author(s): Roy L. Streit; Tod E. Luginbuhl
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Paper Abstract

In a multi-target multi-measurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. In this paper, a strictly probabilistic approach to the measurement-to-track assignment problem is taken. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; instead, the probability that each measurement belongs to each track is estimated using a maximum likelihood algorithm derived by the method of Expectation-Maximization. These measurement-to-track probability estimates are intrinsic to the multi-target tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. Unlike MHT algorithms, the PMHT algorithm does not maintain explicit hypothesis lists. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning.

Paper Details

Date Published: 6 July 1994
PDF: 12 pages
Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); doi: 10.1117/12.179066
Show Author Affiliations
Roy L. Streit, Naval Undersea Warfare Ctr. (United States)
Tod E. Luginbuhl, Naval Undersea Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 2235:
Signal and Data Processing of Small Targets 1994
Oliver E. Drummond, Editor(s)

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