Professor Holzinger's creative efforts addressing autonomy, controls & dynamical systems, and perception focus on a scholarly `theory to hardware' approach, wherein theoretical investigations are confirmed through empirical observations of on-orbit space objects.
To accomplish this, he has conscientiously incorporated real data collection in his program by constructing the Georgia Tech Space Object Research Telescope (GT- SORT), the Omnidirectional Space Situational Awareness (OmniSSA) Array, and working as the principal investigator for an Air Force cubesat program (RECONnaissance of Space Objects, RECONSO).
This combination of empirical data generation assets makes Dr. Holzinger's lab exceedingly unique, generating graduate students with strong theoretical foundations in astrodynamics, controls, and sensor systems, while also comfortable manipulating raw sensor data and operating the electro-optical assets themselves.
The theory-to-hardware approach to research used in Dr. Holzinger's lab allows his group to conceive novel engineering science contributions and maximize national and international impact by developing related technologies and testing them in operational settings.
A result of this approach is an increased awareness and respect for challenging operational issues, which in turn improves the relevance of our theoretical investigations. A selection of Dr. Holzinger's current research application areas are briefly described below.
Space Domain Awareness and Space Traffic Management
Space Domain Awareness (SDA) is best defined as encompassing "the effective identification, characterization and understanding of any factor associated with the space domain that could affect space operations and thereby impact the security, safety, economy, or environment of the United States. [Ref]"
Prof. Holzinger's work in SDA builds upon first principles in dynamics, control, estimation, and decision theory to rigorously develop novel algorithms to intelligently task sensors, process raw electro-optical imagerly, and detect, track, and characterize space objects, and provide decision-support systems delivering actionable information to decision-makers that enables informed, high-quality decisions. This research involves heavy use of mathematics, programming, and empirical data collection and processing (most often using our own telescopes here at CU Boulder).