Responsive Space Situation Awareness in 2020

The U.S. strategy to assure freedom of access in space hinges on Space Situation Awareness (SSA): the ability to find and track space objects and determine their capability and intent. As a result, AFSPC is investing much to overhaul the aging sensors, network the sensors to enable data sharing and...

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Bibliographic Details
Main Author: Teehan, Russell F
Format: Report
Language:English
Subjects:
AGING(MATERIALS)
Astronautics
AWARENESS
COGNITION
COMMAND AND CONTROL SYSTEMS
DATA MANAGEMENT
DETECTORS
DISTRIBUTION
MILITARY STRATEGY
NETWORK CENTRIC WARFARE
NETWORKS
SPACE OBJECTS
SPACE SURVEILLANCE
SSA(SPACE SITUATION AWARENESS)
TIMELINESS
TRAINING
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Summary:The U.S. strategy to assure freedom of access in space hinges on Space Situation Awareness (SSA): the ability to find and track space objects and determine their capability and intent. As a result, AFSPC is investing much to overhaul the aging sensors, network the sensors to enable data sharing and dissemination timeliness, and improve the tactics, techniques, and procedures required to integrate space surveillance into the command and control operations at the Joint Space Operations Center. Regardless, AFSPC is projecting a shortfall in deep space characterization and SSA responsiveness at the end of the mid-term planning cycle in 2020. The goal of this research paper is to recommend a few strategy refinements and a key technology investment necessary to erase these shortfalls. The recommended strategy refinements include: seeking out more contributing sensors, establishing a layered network to free up dedicated sensors to monitor high interest objects and respond to events, using all means to erase the lost? object list, and switching some SSA missions from persistent to routine for the sake of reducing cost and complexity. Though the added sensors and planned net centricity greatly improve coverage and shared situation awareness, the complexity of the network in 2020 and timeliness required to respond to tactical events suggest the need for shared division of labor between humans and machines. Humans must transform from looking at the network as a data provider and instead look at the network as a teammate capable of sharing in the decisionmaking. This paper recommends investment in artificial cognition technology and outlines the training program required to transform the network from the new kid on the block to the seasoned grey beard capable of sharing cognition in some instances and taking over cognition and directing responsive operations when complexity and timelines necessitate it.