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This Defense Intelligence Reference Document, DIA-08-1101-001, is dated 15 December 2010. The Defense Intelligence Agency's Defense Warning Office produced it under the Advanced Aerospace Weapons System Applications program. It asks how many unmanned spacecraft one pilot could control in a future deep-space fleet, drawing on air traffic control and multiple unmanned vehicle research. It concludes the limits are about 16 craft for simple tasks, 7 for moderately complex ones and 4 for complex heterogeneous craft. It adds that physiological measures can signal operator overload.
From the source:Release of 2026-09-18 Incident: 12/15/10, Las Vegas, Nevada. Released with redactions. This document is a Defense Intelligence Reference Document (DIRD), a technical reference format used by the Defense Intelligence Agency (DIA) to capture baseline knowledge on a specific topic for later analytic use. DIRDs are best understood as reference and synthesis products rather than as original research. It is one of 38 DIRDs produced under the Advanced Aerospace Weapon System Applications Program (AAWSAP) between 2009 and 2011. Because AAWSAP’s scope permitted a broad range of supporting topics, not every DIRD in the series directly concerns aerospace systems or future threat assessment. The following summary reflects the DIRD’s scope and framing at the time of writing and should not be read as implying current validation of the concepts discussed. This DIRD examines how many unmanned spacecraft a single human operator could realistically supervise or control at once, using research from air traffic control and multi-vehicle remote piloting as rough analogs. The report argues that the practical limit depends heavily on task complexity: about 16 craft for simple monitoring or destination assignment, about 7 for moderately complex piloting or mission tasks, and about 4 for complex heterogeneous operations. It places particular emphasis on the operator’s ability to maintain a coherent mental “big picture” of multiple vehicles at once, and it suggests that automation and external displays can help by offloading working-memory demands, though not eliminating them. The document also highlights physiological workload measures as a possible way to detect or predict operator overload in real time. Overall, it presents multi-spacecraft control as a human-factors and systems-integration problem in which progress depends on managing cognitive limits through interface design, automation, and workload monitoring.
“Sanderson”1 page
UNCLASSIFIED/ /FOR OPPICIJ!tt tJ!H! 8HLY 36 Loft, S., Sanderson, P., Neal, A. & Mooij, M. Modeling and predicting mental workload in en route air traffic control: critical review and broader implications. Hum Factors 49, 376-399 (2007). 37 Hendy, K. C., Liao, J. & Milgram, P. Combining time and intensity effects in assessing operator information-processing load. Hum Factors 39, 30-47 (1997). 38 Boone, J. 0. Toward the development of a new aptitude selection test battery for air traffic control specialists. Aviat Space Environ Med 51, 694-699 (1980). 39 Cohen, D., Wherry, R. J., Jr. & Glenn, F. Analysis of workload predictions generated by multiple resource theory. Aviat Space Environ Med 67, 139-145 (1996). 40 Hancock, P. A. & Szalma, J. L. Performance under stress. (Ashgate Pub., 2008). 41 Jani, C. & Wickens, C. D. Factors affecting task management in aviation. Hum Factors 49, 16-24 (2007). 42 Wickens, C. & Colcombe, A. Dual-task performance consequences of imperfect alerting associated with a cockpit display of traffic information. Hum Factors 49, 839-850 (2007). 43 Lansdown, T. C., Brook-Carter, N. & Kersloot, T. Distraction from multiple in vehicle secondary tasks: vehicle performance and mental workload implications. Ergonomics 47, 91-104, doi:10.1080/00140130310001629775 (2004). 44 Pack, D. J., Delima, P., Toussaint, G. J. & York, G. Cooperative control of UAVs for localization of intermittently emitting mobile targets. IEEE Trans Syst Man Cybern B Cybern 39, 959-970, doi: 10.1109/TSMCB.2008.2010865 (2009). 45 Wang, J., Qu, Z. H., Ihlefeld, C. M. & Hull, R. A. A control-design-based solution to robotic ecology: Autonomy of achieving cooperative behavior from a high- level astronaut command. Autonomous Robots 20, 97-112, doi:10.1007/s10514- 006-5942-5 (2006). 46 Dixon, S. R., Wickens, C. D. & Chang, D. Mission control of multiple unmanned aerial vehicles: a workload analysis. Hum Factors 47, 479-487 (2005). 47 Lee, J. H., Lee, B. H. & Choi, M. H. A real-time traffic control scheme of multiple AGV systems for collision free minimum time motion: A routing table approach. Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans 28, 347- 358 (1998). 48 Rouse, W. B. Systems engineering models of human-machine interaction. (North Holland, 1980). 49 Cummings, M. L. & Guerlain, S. Developing operator capacity estimates for supervisory control of autonomous vehicles. Human Factors 49, 1-15 (2007). 50 Kornguth, S. E., Steinberg, R. & Matthews, M. D. Neurocognitive and physiological factors during high-tempo operations. (Ashgate, 2010). 51 Cummings, M. L. & Mitchell, P. J. Predicting controller capacity in supervisory control of multiple UAVs. Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans 38, 451-460, doi: 10.1109/tsmca.2007.914757 (2008) . 52 Cummings, M. L., Clare, A. & Hart, C. The Role of Human-Automation Consensus in Multiple Unmanned Vehicle Scheduling. Human Factors 52, 17-27, doi: 10.1177/0018720810368674 (2010). UNCLASSIFIED/ /FOR OliFICil.t.k WSIE 8HLY 26
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Official release, from the pursue collection. The PDF is mirrored here; the original link is above. 31 pages are in the text index: search them above, or from the library's search.