Steindl, Nair, Slavin, Barba, Wood, Jah, "Developing Detectability, Identifiability, Trackability Analyses for the Space Sustainability Rating," IAA-UT Space Traffic Management Conference, STM 2021, 26-27 January 2021, Austin TX USA

Abstract

Three of the core activities in maintaining Space Situational Awareness (SSA) efforts are the detection, identification, and tracking of anthropogenic space objects (ASOs). For much of the space age, the onus for improving global SSA has fallen primarily on the remote sensing community, leading to more technically advanced and powerful sensing systems. With the focus on improving sensor design for SSA purposes, designers have been able to push the envelope of how small their ASOs can be before maintaining adequate knowledge of them becomes too difficult. While these ventures in the use of nanosatellite and picosatellite architectures have been successful proofs of concepts, the proliferation of these small ASOs has made it easier than ever to add to the orbital population while also stretching thin the increasingly taxed sensing systems that the world depends on for SSA. With the number of ASOs in orbit increasing quickly, effort is required of both the sensing and satellite communities to ensure that humans can maintain adequate SSA for the foreseeable future. To aid in these efforts, a team at MIT and the University of Texas at Austin has been working to develop a set of so-called Detectability, Identifiability, and Trackability (DIT) analyses to quantitatively assess how difficult a given ASO’s physical design and orbit are to detect, identify, and track from the Earth. The Detectability analysis utilizes geometric approximations of an ASO, along with its intended orbital parameters, to produce both estimates of its visual magnitude and probability of detection by radar, in order to determine whether or not an ASO is likely to be detectable by an assumed set of sensing capabilities with moderate performance. The Trackability analysis is based on analysis of how the ASO’s orbit interacts with a generically defined ground sensor network over time. Utilizing access statistics for both optical and radar sensing modes, the Trackability analysis is able to delineate varying levels of tracking difficulty for different ASOs. Finally, for the Identifiability analysis the DIT team has been exploring a new approach utilizing cluster analysis based on ASO orbital angular momentum data. Currently this analysis is limited to the population size data for each cluster, but work is underway to incorporate ASO characteristic data. The goal of including characteristic data is to allow the analysis to compare how similar or distinct a given ASO is from others in its ‘orbital zip code’. This paper delves into the specifics of the analysis and discusses the DIT team’s current plans for its implementation. While still a work-in-progress, the team is hard at work to address the current limitations of the analysis and improve its functionality. The DIT team has also been working closely with the developers of the Space Sustainability Rating (SSR) designed by a consortium of organizations including the World Economic Forum, MIT, European Space Agency, the University of Texas at Austin and Bryce Space & Technology. The DIT analysis will be included in one of the six analysis modules of the SSR used to evaluate the efforts of space mission operators to reduce space debris and avoid collisions on orbit.