Session
Session VI: FJR Student Competition
Location
Utah State University, Logan, UT
Abstract
The Kessler syndrome presents a scenario in which space debris produced from an orbital collision increases the risk of further collisions, resulting in an exponential growth of on-orbit debris. With the proliferation of Resident Space Objects (RSOs) in the near-Earth space environment, continual development of ground-based and space-based detection, monitoring, and cataloguing systems is paramount in mitigating the risks associated with collisions. Currently, cataloguing RSOs primarily utilizes ground-based optical and radar systems, however, their performance is impacted and constrained by weather and atmospheric distortions.
As a result of the challenges and limitations of ground-based observations, there is increasing demand for space-based RSO detection and monitoring by means of dedicated satellites and constellations, however, this approach requires significant upfront investment to realize. An alternative approach is the opportunistic use of star trackers, which are already used for attitude determination onboard many satellites. Collectively, by leveraging the naturally expanding and replenishing population of pre-existing flight hardware, these sensors can be used as an extensive network to collect on-orbit Space Situational Awareness (SSA) data without any significant investment. This approach mitigates the constraints of traditional ground-based RSO detection systems while also reducing the cost inherent in deploying a dedicated space-based system.
An RSO within the Field of View (FOV) of a star tracker can be detected and characterized through the application of computer vision and astronomy algorithms applied both within the on-board software and through the analysis of the downlinked data. An algorithm is developed for on-board RSO detection, allowing for the necessary characterization data to be compiled and downlinked for further analysis. The proposed detection algorithm utilizes a single-frame detection approach, in which the geometry and orientation of the observed signals are analyzed to differentiate the RSOs from the background stars. Combined with knowledge of the observer’s position and velocity, sequential detections of individual RSOs provides insight into their Keplerian orbital elements. To validate the effectiveness of the proposed algorithms, a series of synthetic and real night sky images are captured, and the algorithm is utilized to detect the observed RSOs.
Using Star Trackers to Improve Space Situational Awareness
Utah State University, Logan, UT
The Kessler syndrome presents a scenario in which space debris produced from an orbital collision increases the risk of further collisions, resulting in an exponential growth of on-orbit debris. With the proliferation of Resident Space Objects (RSOs) in the near-Earth space environment, continual development of ground-based and space-based detection, monitoring, and cataloguing systems is paramount in mitigating the risks associated with collisions. Currently, cataloguing RSOs primarily utilizes ground-based optical and radar systems, however, their performance is impacted and constrained by weather and atmospheric distortions.
As a result of the challenges and limitations of ground-based observations, there is increasing demand for space-based RSO detection and monitoring by means of dedicated satellites and constellations, however, this approach requires significant upfront investment to realize. An alternative approach is the opportunistic use of star trackers, which are already used for attitude determination onboard many satellites. Collectively, by leveraging the naturally expanding and replenishing population of pre-existing flight hardware, these sensors can be used as an extensive network to collect on-orbit Space Situational Awareness (SSA) data without any significant investment. This approach mitigates the constraints of traditional ground-based RSO detection systems while also reducing the cost inherent in deploying a dedicated space-based system.
An RSO within the Field of View (FOV) of a star tracker can be detected and characterized through the application of computer vision and astronomy algorithms applied both within the on-board software and through the analysis of the downlinked data. An algorithm is developed for on-board RSO detection, allowing for the necessary characterization data to be compiled and downlinked for further analysis. The proposed detection algorithm utilizes a single-frame detection approach, in which the geometry and orientation of the observed signals are analyzed to differentiate the RSOs from the background stars. Combined with knowledge of the observer’s position and velocity, sequential detections of individual RSOs provides insight into their Keplerian orbital elements. To validate the effectiveness of the proposed algorithms, a series of synthetic and real night sky images are captured, and the algorithm is utilized to detect the observed RSOs.
