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Event

Space Enabled Presents at 2021 IAA/UT, Austin Space Traffic Management Conference

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University of Texas at Austin & International Academy of Astronautics

University of Texas at Austin

Tuesday — Wednesday
January 26, 2021 —
January 27, 2021

The Space Security and Safety (SSS) Program at The University of Texas at Austin’s Strauss Center and the Cockrell School of Engineering, in partnership with the International Academy of Astronautics (IAA) and Lockheed Martin, will hold the 7th annual Space Traffic Management conference, “Trust, But Verify: Incentivizing Compliance Through Shared Monitoring and Assessment.” The conference will take place virtually via Zoom webinar on Tuesday, January 26, 2021, and Wednesday, January 27. The conference is being organized by SSS Program Lead Dr. Moriba Jah, and non-resident SSS scholar Dr. Diane Howard.

Tuesday, January 26: Miles Lifson Presents An Advance Market Commitment Program for Low Earth Orbit Active Debris Removal

10:40 – 11:10 am CST Q&A Session 2: Lifetime, ADR, Disposal, and Demise

This paper discusses how the Advance Market Commitment (AMC) concept—developed by the global health community to incentivize development and rapid adoption of vaccines targeted at the needs of low income countries—could be applied as a policy lever to speed the development of private-sector Active Debris Removal (ADR) capabilities and encourage deployment at socially efficient, rather than laissez-faire market-clearing levels.  AMCs work (at least in one possible formulation) by offering a fixed per-quantity subsidy to offerors of a good or service up to a certain quantity in return for an agreement from those offerors to then offer a subsequent quantity to the market at close to their marginal cost of production.  The idea is that the AMC mechanism can potentially help induce new entrants and investment, reduce time to market for solutions, and bring the level of a purchased good/service with diffuse positive externalities closer to the social equilibrium quantity.  This work describes how the AMC concept and associated best practices could be adapted to an ADR AMC and offers various recommendations for program design, implementation, and technical requirements for safety, efficacy, and usability.  It then argues that an ADR AMC program would be consistent with U.S. space policy and should be considered by a U.S. government agency if tasked with a space environment management mission. Future work is proposed to determine potential AMC pricing, understand possible market power dynamics in the ADR market, and to conduct formal economic modeling responsive to the specifics of an ADR AMC.

Wednesday, January 27: Riley Steindl Presents "Developing Detectability, Identifiability, and Trackability Analyses for the Space Sustainability Rating"

8:50 – 9:20 am CST Q&A Session 3: SSA Technologies

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.

Wednesday, January 27: Minoo Rathnasabapathy Presents "Links from the Space Sustainability Rating to Space Safety Concerns at Each Mission Lifecycle"

10:30 – 11:00 am CST Q&A Session 4: Behavior Incentivizing, Monitoring, and Assessment

The Space Sustainability Rating is an initiative of the World Economic Forum’s Global Future Council on Space Technology which aims to create an incentive system that celebrates the efforts of satellite mission operators who work to reduce the likelihood of space debris and collisions among space objects. The Space Sustainability Rating has been under development by a consortium of  experts including representation from the World Economic Forum, the European Space Agency, the University of Texas at Austin, Bryce Space and Technology and the Massachusetts Institute of Technology. In the future the SSR will be operated by an entity selected via competitive proposals to issue the ratings to operators who apply.

The logic of the Space Sustainability Rating is that the design team identified six modules that capture specific aspects of behavior by satellite operators including Data Sharing, Collision Avoidance, Standards & Regulations, the Detectability, Identifiability and Trackability of the space object, and a Mission Index which estimates the Space Traffic Footprint or the impact a space object has on the risk profile of the orbital environment and other objects. This paper explains how the satellite operator makes decisions at each lifecycle phase to influence the Space Sustainability Rating. By considering the lifecycle – from early design, to detailed design to Assembly, Integration and Test to launch to early operations, mission operators and disposal – this paper highlights the opportunities that each satellite mission operator has to improve their SSR score and reduce risk of collisions or debris creation. The discussion shows show the SSR accounts for concepts including Anthropogenic Space Objective Identification and classification, Space Objective Data Sharing, Spacecraft Anomalies and Mission Assurance through quantitative modeling and qualitative metrics.

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