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Improving space object detection using a Fourier likelihood ratio detection algorithm
Author(s): David J. Becker; Stephen C. Cain
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Paper Abstract

In this paper a new detection algorithm is proposed and developed for detecting space objects from images obtained using a ground-based telescope with the goal to improve space situational awareness. Most current space object detection algorithms rely on developing a likelihood ratio test (LRT) for the observed data based on a binary hypothesis test. These algorithms are based on the assumption that the observed data is Gaussian or Poisson distributed under both the hypothesis that a low signal-to-noise ratio (SNR) space object is present in the data and the hypothesis that an object is absent from the data. The LRT algorithm in this paper was developed based on the assumption that the distribution of the Fourier transform of the observed data will be different when a low SNR object is present in the data compared to when the data only contains background noise and known space objects. When an object is present the probability distribution of the real component of the Fourier transform of the intensity was found to follow a Gaussian distribution with a mean significantly different than in the data that doesn’t contain an object even at low SNR levels. As the separation of these two probability distribution functions increases, it becomes more likely that an object can be detected. In this paper, simulated data are used to demonstrate the effectiveness and to highlight the benefits gained from this algorithm.

Paper Details

Date Published: 20 September 2016
PDF: 14 pages
Proc. SPIE 9982, Unconventional Imaging and Wavefront Sensing XII, 99820L (20 September 2016); doi: 10.1117/12.2236992
Show Author Affiliations
David J. Becker, Air Force Institute of Technology (United States)
Stephen C. Cain, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9982:
Unconventional Imaging and Wavefront Sensing XII
Jean J. Dolne; Thomas J. Karr; David C. Dayton, Editor(s)

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