Development of a real-time neural network estimator to improve defense capabilities of HEO satellites

Samuel Lightstone; Moshe Fink; Fred Moshary; Barry Gross

doi:10.1117/12.2220073

17 May 2016 Development of a real-time neural network estimator to improve defense capabilities of HEO satellites

Samuel Lightstone, Moshe Fink, Fred Moshary, Barry Gross

Proceedings Volume 9828, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XIII; 98280G (2016) https://doi.org/10.1117/12.2220073
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

The need to observe thermal targets from space is crucial for monitoring both natural events and hostile threats¹. Satellite design must balance high spatial resolution with high sensitivity and multiple spectral channels². Defense satellites ultimately choose high sensitivity with a small number of spectral channels. This limitation makes atmospheric contamination due to water vapor a significant problem that cannot be determined from the satellite itself. Using a neural network (NN) approach in conjunction with real time measurements or model predictions of sounding data, we show that an accurate estimation of band radiation and band transmission can be computed in near real time. To demonstrate accuracy, we compare the neural network outputs to both model atmospheres as well as the Modis data for a suitable water vapor band.

Citation Download Citation

Samuel Lightstone, Moshe Fink, Fred Moshary, and Barry Gross "Development of a real-time neural network estimator to improve defense capabilities of HEO satellites", Proc. SPIE 9828, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XIII, 98280G (17 May 2016); https://doi.org/10.1117/12.2220073

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KEYWORDS

Neural networks

MODIS

Satellites

Atmospheric modeling

Data modeling

Missiles

Transmittance

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