Improved Navigation for Highly Dynamic Environments

Spacecraft missions are more commonly entering highly dynamic environments, such as lunar libration orbits, orbits about small bodies, and orbits driven by low-thrust propulsion. Advanced Space proposes to develop a robust navigation filter optimized for each of these highly dynamic environments. Further, the tool will be designed to be open-source and work with tools such as NASA's General Mission Analysis Tool (GMAT). The dynamic filter will implement state-of-the-art process noise strategies tuned for dynamic environments. These process noise strategies, combined with an extended Kalman filter, will permit the filter to operate on very long arcs without resets, to operate on unstable trajectories, and to operate on trajectories propagated in undetermined systems, such as in the vicinity of an uncharacterized asteroid. The filter will estimate unmodeled accelerations, tuned to be sensitive to the uncertainties present in each of these highly dynamic environments.

A purpose-built navigation tool for libration point orbits, low-thrust spacecraft operations, and proximity operations around small bodies is projected to have a significant impact on NASA missions. The primary goal of this work is to infuse this new navigation capability into existing and future NASA mission needs and projects. It is for this reason that the software developed through this work will be designed for use from mission design and analysis through spacecraft operations. Such extendibility requires specific design trades that will be made early in the proposed work to enable users to obtain information during the mission formulation stage and reliable navigation solutions during operations. In highly dynamic environments such as those the software will be optimized for, initial mission planning is significantly influenced by the subsequent capabilities of the operational navigation tools. Examples of this impact include scheduling tracking time on highly stressed tracking stations such as the Deep Space Network (DSN), identifying mission operations in terms of when maneuvers can and cannot occur, particularly for low-thrust missions, and how much time must be given between maneuvers for a navigation solutions to converge. Specific targeted applications include support for future missions to the lunar libration point orbits, any future mission using low-thrust maneuvering over long or very dynamic thrust-arcs, and missions exploring small bodies such as asteroids.

The highly dynamic navigation filter will be designed to support numerous non-NASA commercial customers. Many spacecraft operators are examining low-thrust methods to transfer from low Earth orbit to geostationary orbit (GEO). The filter designed here will support such activities over long periods of time, without restarting or reconfiguring. The filter will estimate more than just the satellite's position and velocity: it will also estimate the spacecraft's mass, maneuver execution parameters, unmodeled accelerations, and other parameters. The GEO market is very large; smaller markets include commercial entities exploring and mining the Moon and asteroids. Each customer in these markets requires navigation; robust, accurate, and reliable navigation is a significant benefit for their businesses. A vetted navigation architecture in an open-source environment that includes state-of-the-art algorithms is expected to serve as a foundation for future operational innovations within commercial spaceflight markets.

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