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Proceedings Paper

Multitarget tracking using sensors with known correlations
Author(s): Ronald Mahler
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Paper Abstract

This paper is the fourth in a series aimed at weakening the independence assumptions that are typically presumed in multitarget tracking. Specifically, we assume that, in a multisensory scenario, the sensors are not necessarily independent but, rather, have known correlations (i.e., their joint single-target joint likelihood function is known). From this, we construct a multitarget measurement model for sensors with known correlations. From this model we derive, as an illustrative example, the filtering equations for a probability hypothesis density (PHD) filter for sensors with known correlations. We emphasize the two-sensor case of this filter, for which the measurement-update equations involve a summation over all measurement-to-measurement associations between the two sensors.

Paper Details

Date Published: 17 May 2016
PDF: 12 pages
Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98420B (17 May 2016); doi: 10.1117/12.2224112
Show Author Affiliations
Ronald Mahler, Random Sets, LLC (United States)


Published in SPIE Proceedings Vol. 9842:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXV
Ivan Kadar, Editor(s)

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