Mark Bolden (Pennsylvania State University), David Spencer (Pennsylvania State University)
Keywords: data fusion, data sharing, space situational awareness, leveraging astronomical data for SSA, survey telescopes,
Abstract:
As the rate of new objects in Earth orbit continues to accelerate due to new launches and debris creation, the data volume required to keep pace rapidly increases utilizing traditional algorithms. Penn State is developing a novel concept to fuse cutting edge star catalogs, radiometry and astrometry to increase the value of the data already available by utilizing every pixel, not just the pixels with detections meeting threshold requirements. This paper will discuss the methodologies of the two major efforts which utilize probabilistic approaches to improve catalog accuracy: Something From Nothing (SFN) and Streak Harvest (SH). SFN is developing algorithms to utilize pixels which have NOT detected an object. SH is developing algorithms to utilize pixels which have detected streaks that have not met the n out of m detection criterion or only partially streak through the field of view resulting in missing endpoints. This paper will also discuss the dangers of utilizing these dataset types. This effort is inspired by the work done by Air Force Research Laboratory with the Pan-STARRS astronomical telescope, un-cued detection efforts such as SST and LILO, and the large volume of wide field astronomical sensors, such as the future LSST, collecting un-utilized SSA data everyday around the world.
Date of Conference: September 9-12, 2014
Track: Sensor Processing