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Proceedings Paper

Using ML-PDA and ML-PMHT to track two unresolved moving objects
Author(s): Katherine Domrese; Peter Willett; Yaakov Bar-Shalom
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Paper Abstract

Both the Maximum Likelihood Probabilistic Data Association (ML-PDA) track extractor and the Maximum Likelihood Probabilistic Multi-Hypothesis (ML-PMHT) track extractor are extended in this work to handle the scenario of two unresolved moving objects in the field of gravity. The original ML-PDA and ML-PMHT log-likelihood ratios are modified to use the probability that the objects being tracked are unresolved i.e., their measurements are merged. The performances of the modified ML-PDA and ML-PMHT, which we denote MLPDA-M and ML-PMHT-M (M for merged), respectively, are compared with those of the ML-PDA and the ML-PMHT in a notional scenario in which two moving objects appear initially unresolved to two space-based passive sensors observing them and become resolved first by one and then by both. Simulation results for the original track extractors and the modified track extractors are presented. While in many tracking situations the performances of the ML-PDA and ML-PMHT are indistinguishable (and the ML-PMHT therefore selected for its other features), this case of challenged resolution appears to be one situation where the more arduous ML-PDA ought to be favored. There does seem to be some reason to favor the “M” versions of both, but the results there are less compelling.

Paper Details

Date Published: 2 May 2017
PDF: 13 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020003 (2 May 2017); doi: 10.1117/12.2264008
Show Author Affiliations
Katherine Domrese, Univ. of Connecticut (United States)
Peter Willett, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)

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