Techniques for Improved Space Object Detection Performance from Ground-Based Telescope Systems Using Long and Short Exposure Images
Date of Award
Doctor of Philosophy (PhD)
Department of Electrical and Computer Engineering
Stephen C. Cain, PhD.
Space object detection is of great importance in the highly dependent yet competitive and congested space domain. Detection algorithms employed play a crucial role in fulfilling the detection component in the space situational awareness mission to detect, track, characterize and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator on long exposure data to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follows a Gaussian distribution. This research focuses on improving current space object detection algorithms and developing new algorithms that provide a greater detection performance, specifically with dim and small objects which are inherently difficult to detect. With a greater detection rate, a great number of unknown objects will be detected, tracked and cataloged to deliver safer space operations. Three novel approaches to object detection using long and short exposure images obtained from ground-based telescopes are examined in this dissertation.
DTIC Accession Number
Becker, David J., "Techniques for Improved Space Object Detection Performance from Ground-Based Telescope Systems Using Long and Short Exposure Images" (2018). Theses and Dissertations. 1915.