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Exploratory model analysis of the Space Based Infrared System (SBIRS) Low Global Scheduler problem

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Author
Morgan, Brian L.
Date
1999-12
Advisor
Lucas, Thomas W.
Second Reader
Read, Robert R.
Gottschalk, Thomas D.
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Abstract
Proliferation of theater ballistic missile technologies to potential U.S. adversaries necessitates that the U.S. employ a defensive system to counter this threat. The system that is being developed is called the Space-Based Infrared System (SBIRS) "System of Systems". The SBIRS Low component of the SBIRS "System of Systems" will track strategic and theater ballistic missiles from launch to reentry and relay necessary cueing data to missile interceptors before the missiles reach friendly forces or countries whose safety is a vital interest to the U.S. SBIRS Low has a number of critical system requirements that for any given satellite are mutually exclusive for the length of time needed to complete the specified tasking. This limitation implies a system capacity on the total number of ballistic objects the SBIRS Low system can track at any given time. Applying exploratory model analysis, the SBIRS Low model uses the Monte Carlo method to explore large regions of the model space to identify key factors in the system and to provide insight into different tasking schemes for individual satellites. The exploratory model analysis, which consisted of 13, 760,000 missiles being tracked in the analysis of the CSS-2 and M-9 missiles, yielded the following significant results: (a) defining the "best" satellite is nontrivial, (b) the SBIRS Low system was unable to initiate a booster track for an unacceptably large percentage of M-9 missiles launched near the equator, (c) if the system anticipates a long delay in revisiting a track, a stereo view should be scheduled immediately prior to the start of the delay, (d) mono viewing alone does not provide the required track accuracy, (e) track accuracy is a function of missile classification, and (f) the instantaneous track accuracy versus sensor revisit rate does not fit an well-known probability distribution
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http://hdl.handle.net/10945/8378
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