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AI and Autonomy Initiatives for NASA’s Deep Space Network (DSN)NASA’s Deep Space Network (DSN) consists of thirteen
large (34- and 70-meter) antennas that are used to communicate
with approximately 40 NASA and partner spacecraft, all
at great distance from the earth (generally at Lunar distances
and beyond). The DSN has a long history — over 50 years
— and has evolved with cutting edge, often custom, telecommunications
equipment and associated software systems. In
recent years, and in preparation for an increasing future demand,
there has been an effort to invest in initiatives that will
result in significant cost savings in the future. These efforts
are building on, or augmenting, the recent deployment of
“Follow-the-Sun” operations (day shift remote operational
control of the entire network from each of the three antenna
complexes in turn) — which is being deployed in 2017. This
paper focuses on Adaptive Demand Access: in a paradigm
shift from completely pre-planned operations, this concept
calls for spacecraft to signal their intent (or not) for near-future
contacts, in case they have science results of interest, or
have experienced an anomaly. This would take advantage of
a beacon tone transmission, which can be detected using
smaller antennas. When a connection request is received, the
DSN ground systems would adaptively accommodate the request,
inserting the contact into the plan as soon as possible,
subject to constraints and priorities. The demand access concept
incorporates onboard data analysis and science data processing,
so that beacon tones can be generated with maximum
information. This area is representative of several where infusing
AI technologies can lead to improved effectiveness of
the DSN as the network readies for support of expanded Mars
exploration efforts in the 2020’s and beyond.
Document ID
20210007801
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Wyatt, E. Jay
Johnston, Mark D.
Date Acquired
August 19, 2017
Publication Date
August 19, 2017
Publication Information
Publisher: Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2017
Distribution Limits
Public
Copyright
Other
Technical Review

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