Generalizing Distributed Missions Design Using the Trade-space Analysis Tool for Constellations (TAT-C) and Machine Learning (ML)

A large amount of Earth Science data must be sustained or augmented for scientific and operational purposes under constrained budget requirements. Multipoint measurement missions can provide a significant advancement in science return at a manageable cost. Coupled with recent technological advances, this science interest drives a trend toward distributed architectures for future NASA missions instead of the traditional monolithic ones. As a general definition, Distributed Spacecraft Missions (DSMs) leverage multiple spacecraft to achieve one or more common goals. In particular, a constellation is the most general form of DSM with two or more spacecraft placed into specific orbit(s) for the purpose of serving a common objective. DSMs are gaining momentum in all science domains and in Earth Science they enable new measurements and simultaneous observation sampling increases in spatial, spectral, temporal and angular dimensions. Additionally, DSMs are expected to increase mission flexibility, scalability, evolvability and robustness, to facilitate data continuity, and to minimize cost risks associated with launch and operations, thus responding to both data needs and budget constraints. However, distributed architectures also carry a risk of being "robust-yet-fragile," a paradoxical behavior where poorly-understood interdependencies lead to unexpected failures. Considering both the upside potential and downside risk of DSMs requires careful evaluation of operations in a simulated environment. Furthermore, a DSM architectural trade-space includes both monolithic and distributed design variables subject to combinatorial factors. As a result, DSM optimization is a large and complex problem with multiple conflicting objectives. Our proposed solution to these challenges develops an open-access tool which will be available to the scientific community for pre-Phase A constellation mission analysis. Over the last two years, our team has developed the prototype Trade-space Analysis Tool for Constellations (TAT-C). By enumerating and evaluating alternative mission architectures, TAT-C minimizes cost and maximizes performance for pre-defined science goals and helps to quantify and evaluate specific DSM challenges such as data calibration. TAT-C is suitable for missions ranging from smallsats to flagships and is based on existing modeling and analysis capabilities developed at NASA Goddard. TAT-C currently addresses basic capabilities required for pre-Phase A constellation design with a general framework and has already proven valuable in analyzing imaging systems. Its implementation provides improved and integrated capabilities compared to existing solutions; it also enables easy addition of new functionality. This proposal will extend TAT-C to broader Earth Science interests, support additional trades on instruments, spacecraft sizes, launch choices and onboard processing hardware and computations, and extend cost and risk analysis to reconsider requirements of ground operations and mission replanning. The increased number of design variables coupled with combinatorial factors associated with DSMs demand a new Trade-Space Search Iterator driven by machine learning (ML) techniques and working closely with a fully functional and populated knowledge base to efficiently explore and optimize over a tractable design space. The final TAT-C ML software developed under this proposal will provide the Earth Science community a powerful tool to quickly design novel DSMs or augment existing missions to optimize their science return. Without TAT-C, missions would either have sub-optimal performance and science return, or each DSM design team would need to develop an equivalent to TAT-C to enable their mission optimization. Our proposed project responds to the Earth Science Technology Office's AIST Program Operations Technologies Core Topic by designing a tool that will perform mission design trade studies and will enable new types of observations.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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