Is this project an undergraduate, graduate, or faculty project?
Undergraduate
individual
Daytona Beach
Authors' Class Standing
Joseph Anderson, Senior Annika Anderson, Senior David Zuehlke, Graduate Student David Canales, Professor Thomas A. Lovell, Professor
Lead Presenter's Name
Joseph Anderson
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
David Canales Garcia
Abstract
Identifying resident space objects (RSOs) in arbitrary space imagery with little a-priori information is a challenging, yet crucial next step in space-domain awareness applications. This work proposes improvements to an existing RSO identification process for unresolved space images. The algorithm has three main phases: image processing, star elimination, and RSO association. Star elimination and RSO association use nearest neighbor association and thresholds on inertial frame-to-frame motion of observations to associate objects. Given a set of unresolved space images contiguous in time, the product of the algorithm presented is a set of measurements for orbit estimation.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
RESIDENT SPACE OBJECT IDENTIFICATION IN ARBITRARY UNRESOLVED SPACE IMAGES
Identifying resident space objects (RSOs) in arbitrary space imagery with little a-priori information is a challenging, yet crucial next step in space-domain awareness applications. This work proposes improvements to an existing RSO identification process for unresolved space images. The algorithm has three main phases: image processing, star elimination, and RSO association. Star elimination and RSO association use nearest neighbor association and thresholds on inertial frame-to-frame motion of observations to associate objects. Given a set of unresolved space images contiguous in time, the product of the algorithm presented is a set of measurements for orbit estimation.