An Operational Strategy to Enhance Real-Time Spacecraft Track Monitoring using Automated Historical Context Generation

The Deep Space Network (DSN), managed by NASA’s Jet Propulsion Laboratory in Pasadena California, is a multinational, cooperative communications infrastructure for deep space communications and science applications. Use cases include tracking, telemetry, and commanding of NASA and partner-agency deep-space spacecraft. In addition to this, select radio astronomy, radio science, and radar activities also utilize the DSN. The network has operated with a high degree of reliability for over fifty years. New challenges are emerging however, including supporting more numerous (and smaller) spacecraft, as well as handling a greater complexity of operations due to increased requirements on network operational staff – specifically link-control operators (LCOs). Additionally, the DSN’s recent adherence to a “follow-the-sun” (FtS) model, where a given Deep Space Communications Complex (DSCC) operates the entire global network during its respective daylight hours, presents potential challenges including in terms of numerosity and diversity of equipment that LCOs need to monitor as more spacecraft and antenna are added to the DSN in the future. These challenges motivate the need for greater automation and operational intelligence tools to streamline and simplify the information the DSN presents to LCOs. In light of these challenges, we present an automation strategy for identifying and highlighting the historical performance context for spacecraft communication link operations. We propose a software called Track Augmentation and Performance Analysis System (TAPAS) to automatically generate analytic comparisons between historical spacecraft communication tracking scenarios (hereafter referred to as “tracks”) compared to real-time tracking scenarios as well as present a user interface strategy to communicate these complex calculations to LCOs in an intuitive way. Through TAPAS, it is our goal to offer a streamlined way for LCOs to simplify and reduce the time it takes to answer at least two key questions about ongoing, live spacecraft tracks: (1) is a given live (reference) track off-nominal compared to a historical baseline of similarly configured tracks, and (2) which segments of the reference track showcase a deviation, if at all, from historical tracks and why.