Lt Stephen Maksim (United States Air Force), Maj J. Chris Zingarelli (United States Air Force), Stephen Cain (Air Force Institute of Technology)
Keywords: Data, Satellite Detection
Abstract:
This paper describes and analyzes a statistical satellite detection algorithm. This detection algorithm exploits the Poisson nature of light and the correlation of pixels that arises from the long-term exposure point spread function (PSF) due to diffraction and atmospheric effects. This new detection algorithm is compared to a simple, linear threshold detection algorithm, which treats the probability density function (PDF) of the photons as Gaussian and the PSF as Dirac. Using a dataset collected by the Space Surveillance Telescope (SST) that shows many samples of a geostationary satellite gradually going into the terminator during the vernal equinox, the probabilities of detection for the algorithms are compared as the satellite becomes very dim and nearly disappears to determine which detection algorithm performs better. A better performing detection algorithm will allow detection of satellites, space debris, and other dim objects that have not been visible before.
Date of Conference: September 11-14, 2012
Track: Data and Services