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Proceedings Paper

Deriving predictive turbulence data models
Author(s): Holger Jaenisch; James Handley; Michael Curley; Matthew Edwards; Jai-Ching Wang
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Paper Abstract

We present a novel algorithm taking measurements of time, solar irradiance, wind speed, peak wind speed, temperature gradient, and relative humidity to derive a predictive differential equation for mean Cn2. Our method derives individual control terms and forcing functions by modeling macro-structure, micro-structure, and fine structure terms independently. The final model is suitable for analysis and able to be used as a baseline expectation model for in situ battlefield use for predictive optical correction or slewing, and possibly for mitigating the effects of wind shear on artillery shells downrange.

Paper Details

Date Published: 24 March 2008
PDF: 12 pages
Proc. SPIE 6971, Acquisition, Tracking, Pointing, and Laser Systems Technologies XXII, 69710H (24 March 2008); doi: 10.1117/12.775561
Show Author Affiliations
Holger Jaenisch, Alabama A&M Univ. (United States)
Licht Strahl Engineering, Inc. (United States)
James Handley, Licht Strahl Engineering, Inc. (United States)
Michael Curley, Alabama A&M Univ. (United States)
Matthew Edwards, Alabama A&M Univ. (United States)
Jai-Ching Wang, Alabama A&M Univ. (United States)


Published in SPIE Proceedings Vol. 6971:
Acquisition, Tracking, Pointing, and Laser Systems Technologies XXII
Steven L. Chodos; William E. Thompson, Editor(s)

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