A Predictive Model for Multi-Band Optical Tracking System (MBOTS) Performance
Author
Horii, M. MichaelAffiliation
Photo-Sonics, Inc.Issue Date
2013-10
Metadata
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Copyright © held by the author; distribution rights International Foundation for TelemeteringCollection Information
Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.Abstract
In the wake of sequestration, Test and Evaluation (T&E) groups across the U.S. are quickly learning to make do with less. For Department of Defense ranges and test facility bases in particular, the timing of sequestration could not be worse. Aging optical tracking systems are in dire need of replacement. What's more, the increasingly challenging missions of today require advanced technology, flexibility, and agility to support an ever-widening spectrum of scenarios, including short-range (0 − 5 km) imaging of launch events, long-range (50 km+) imaging of debris fields, directed energy testing, high-speed tracking, and look-down coverage of ground test scenarios, to name just a few. There is a pressing need for optical tracking systems that can be operated on a limited budget with minimal resources, staff, and maintenance, while simultaneously increasing throughput and data quality. Here we present a mathematical error model to predict system performance. We compare model predictions to site-acceptance test results collected from a pair of multi-band optical tracking systems (MBOTS) fielded at White Sands Missile Range. A radar serves as a point of reference to gauge system results. The calibration data and the triangulation solutions obtained during testing provide a characterization of system performance. The results suggest that the optical tracking system error model adequately predicts system performance, thereby supporting pre-mission analysis and conserving scarce resources for innovation and development of robust solutions. Along the way, we illustrate some methods of time-space-position information (TSPI) data analysis, define metrics for assessing system accuracy, and enumerate error sources impacting measurements. We conclude by describing technical challenges ahead and identifying a path forward.Sponsors
International Foundation for TelemeteringISSN
0884-51230074-9079