Using ML-PDA and ML-PMHT to track two unresolved moving objects

Katherine Domrese; Peter Willett; Yaakov Bar-Shalom

doi:10.1117/12.2264008

2 May 2017 Using ML-PDA and ML-PMHT to track two unresolved moving objects

Katherine Domrese, Peter Willett, Yaakov Bar-Shalom

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 1020003 (2017) https://doi.org/10.1117/12.2264008
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Citation Download Citation

Katherine Domrese, Peter Willett, and Yaakov Bar-Shalom "Using ML-PDA and ML-PMHT to track two unresolved moving objects", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020003 (2 May 2017); https://doi.org/10.1117/12.2264008

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KEYWORDS

Sensors

Detection and tracking algorithms

Signal to noise ratio

Data modeling

Personal digital assistants

Error analysis

Mathematical modeling

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