Maximum likelihood method for probabilistic multihypothesis tracking

Roy L. Streit; Tod E. Luginbuhl

doi:10.1117/12.179066

6 July 1994 Maximum likelihood method for probabilistic multihypothesis tracking

Roy L. Streit, Tod E. Luginbuhl

Proceedings Volume 2235, Signal and Data Processing of Small Targets 1994; (1994) https://doi.org/10.1117/12.179066
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Roy L. Streit and Tod E. Luginbuhl "Maximum likelihood method for probabilistic multihypothesis tracking", Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); https://doi.org/10.1117/12.179066

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