KEYWORDS: Clouds, Mid-IR, Long wavelength infrared, Temperature metrology, Data modeling, Atmospheric modeling, Infrared search and track, Target detection, Humidity, Emissivity
The presence of clouds affects the detection of small airborne targets for infrared imaging. Clouds increase the signal of the background and create nonuniformity behind a desired target. This results in low and varying contrast. Clear sky conditions provide a low noise, uniform background that gives a better chance of detection. Understanding key variables of the clouds nonuniform structure allows for better detection and for accurate infrared search and track (IRST) models. Atmospheric modeling software, such as moderate resolution atmospheric transmission (MODTRAN), provides background path radiance in the emissive midwave infrared and longwave infrared bands. These modeled skies have been matched with measured skies in various conditions with low error. MODTRAN clouds, however, assume total cloud cover of uniform thickness and no varying transmission. MODTRAN clouds do not consider the spatially and radiometrically varying structures that make clouds unique. Studied spatial and radiometric characteristics of clouds are used in an empirical approach to predict cloud radiometric temperatures and structures with four simple equations. These cloud properties are measured at night to avoid solar contributions and focus on their emissive characteristics. The empirically modeled clouds are projections from measured or MODTRAN modeled clear skies. This method of modeling clouds allows for easy implementation of a nonclear sky background into IRST models. The range in which a target is first detected from its background can now be compared between clear and cloudy skies.
KEYWORDS: Clouds, Target detection, Mid-IR, Long wavelength infrared, Infrared radiation, Infrared detectors, Signal to noise ratio, Infrared signatures, Black bodies
Clouds can increase the signal of the background and create non-uniformity behind an airborne target which results in low and varying contrast. Clear sky conditions provide a low noise, uniform background that gives a better chance of detection. In comparison, clouds in the immediate vicinity of a target can decrease the signal to noise ratio (SNR). Understanding key variables of this non-uniform structure can allow for better detection of small UAVs. The presented radiometric and spatial characteristics for both the midwave and longwave bands are the maximum and minimum blackbody equivalent temperature and the distributions of the cloud temperatures. The spatial metrics of measurements are a one-dimensional power spectrum to understand the random spatial structure of the clouds. These cloud properties are measured at night to avoid any solar contributions and obtain their emissive characteristics. An Empirical Model is created to predict cloud radiances in any atmosphere.
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