Monte Carlo Sets Of Synthetic Infrared Background Scenes

V. J. Pecora Jr.; A. T. Maksymowicz; L. H. Wald

doi:10.1117/12.934239

12 July 1983 Monte Carlo Sets Of Synthetic Infrared Background Scenes

V. J. Pecora Jr., A. T. Maksymowicz, L. H. Wald

Proceedings Volume 0366, Modern Utilization of infrared Technology VIII; (1983) https://doi.org/10.1117/12.934239
Event: 26th Annual Technical Symposium, 1982, San Diego, United States

Abstract

Synthetic background scenes can play a significant role in the development of space surveillance systems and autonomous navigation. They can be used to test the performance of proposed target acquisition and tracking algorithms, to evaluate the robustness of navigation algorithms with respect to temporal scene variations, and to investigate the scope of on-board data processing requirements. They can provide a more thorough and systematic evaluation of the relative performance of competing algorithms for a greater diversity of terrain backgrounds, environmental conditions, and viewing geometries than real data. Moreover, their use requires no costly commitment to hardware construction and flight programs. We describe a comprehensive program for the generation of Monte Carlo sets of background scenes, statistically representative of the diverse environmental conditions for a particular locale. Sample results are given of a Monte Carlo simulation of clouds over ocean at midlatitudes. In addition to graphic representations of the infrared radiance distributions, we outline the simulation methodology and the meteorological data base used for the statistical modeling of cloud cover. As an application we use this set of synthetic scenes to evaluate the performance of a drift-compensated temporal differencing algorithm for clutter suppression.

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V. J. Pecora Jr., A. T. Maksymowicz, and L. H. Wald "Monte Carlo Sets Of Synthetic Infrared Background Scenes", Proc. SPIE 0366, Modern Utilization of infrared Technology VIII, (12 July 1983); https://doi.org/10.1117/12.934239

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KEYWORDS

Clouds

Monte Carlo methods

Sensors

Atmospheric modeling

Scene simulation

Detection and tracking algorithms

Meteorology

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