Space Enabled members Minoo Rathnasabapathy, Elena Cirkovic, and Maya Slavin will present at this year's AIAA Ascend conference, being held live and virtually accessible in Las Vegas. Launched by AIAA, ASCEND brings people together to discuss fundamental questions and deliver solutions that will advance the global space community. They combine technical and non-technical content in a highly engaging and intentional way so attendees can play an active role in contributing to the building of our off-world civilization.

Monday, November 15 - 4:00pm-5:00pm ET

Maya Slavin presents "Use of ASTRIAGraph to inform Detection, Identification and Tracking metrics for space sustainability". Co-authors include Professor Danielle Wood and Prof Moriba Jah.

Session: Aerospace Traffic Management TECH.ATM-01

In order to facilitate a safe, sustainable operating environment in space, the space community must maintain a sufficient level of space situational awareness (SSA). Traditionally, the responsibility for doing so has fallen on sensor networks to collect observations, correlate them with a known space object, and update their tracking catalog. This process means that the quality of SSA is directly tied to the technological capabilities and accuracy of the sensors and processing algorithms. We would like to propose that there are ways for satellite operators to contribute to improved SSA through certain design decisions they can make with their mission. The general SSA process can be broken up into 3 steps - detection, identification, and tracking (DIT). A methodology was then created that assigns satellites a score in each of these categories to represent how well they are able to be detected, identified, and tracked. A high score indicates that a satellite operator has designed a mission with a spacecraft and orbital operations that contribute to reducing collision risk, reducing the residence time of debris and supporting long term sustainability of the orbital environment. The scores are designed to be independent of ground-based sensor capabilities, so all calculations are done with a simulated, uniform ground-based radar and optical sensor network of moderate sensing performance. The sensor network design is benchmarked based on the commercially available ground-sensor capabilities. For the Detectability score, a geometric approximation of the satellite and the orbital information are used to estimate the satellite’s average visual magnitude from an optical sensor and probability of detection by a radar sensor. These two values are an approximation for how likely it is that a satellite could be detected by ground-based sensors, which is the first step in incorporating it into SSA efforts. For the Identifiability score, several distinguishing features of the satellite (radar cross-section, dimensions, altitude, angular momentum, and visual magnitude) are used to quantify how hard it is to identify the satellite. This process is still being developed but the score will be calculated by querying ASTRIAGraph, a graph database source that combines data from multiple SSA providers developed by Prof Moriba Jah at the University of Texas at Austin, to count how many other space objects share these characteristics with the satellite being scored. This, along with information about the operator’s data sharing practices, will provide a gauge of how difficult it might be to identify the satellite from sensor observations. Identification is an important step of SSA because it allows for better communication about collision avoidance and coordination if the satellite operator is known and can be contacted. For the Trackability score, the orbital information and simulated sensor network are used to calculate the average length of an access opportunity where the satellite can be observed by a sensor, the average interval of time between opportunities, and an approximation of what percentage of the orbit can be observed. These metrics were chosen because the more often the satellite can be observed, and the shorter the interval between these opportunities, the more accurate the tracking capability will be. Tracking is an essential part of the SSA process because it is what allows for the continual updating of the locations of satellites after initial detection and identification. The foundations of the DIT scoring methodology and the cutoffs for different scores were developed with case studies of existing space missions that have publicly available information about their physical characteristics and orbits. To further refine the methods, the team conducted beta tests and assigned scores for several actual operators who were willing to provide information about their proposed missions. This beta testing provided valuable feedback on the detectability and trackability methods, while continuing to develop the identifiability scoring process. We plan to continue adjusting the methods so that the DIT scores reflect an informative picture of how the operator’s design decisions contribute to SSA efforts and thus, space sustainability in general. The DIT methodology was developed as part of the Space Sustainability Rating (SSR), which is a broader project to create a scoring system for how sustainable a given space mission is and to give satellite operators more awareness about the impact of their design decisions. The SSR is being designed by a consortium that includes the Massachusetts Institute of Technology, the European Space Agency, the University of Texas at Austin, and Bryce Space and Technology. The DIT scores will be just one of six modules that makes up the overall SSR score, with each module addressing a different aspect of the mission’s sustainability.