Documents / Official release
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.
UNCLASSIFIED/lfQR OEFICitk W&li 8HLY The more recent Collet study recorded 5 ANS variables from 25 participants during real ATC operations. The population, mean age of 44, included only fully qualified operators, who were monitored for one hour during TRACON duty at Saint Exupery International Airport (Lyon, France) . Correlation analyses were performed with the number of aircraft the operator was currently controlling . No adjustment was made for task complexity; however, data were acqu ired between 6 and 9 PM local time to collect medium and high workload data . Each participant handled between 1 and 10 aircraft during the study. The results of the correlation analysis are shown in Table 2. The authors conclude that changing the number of aircraft for professional ATCs produced correlations in physiological measures for SC, SBF, and IHR. 31 Table 2. Correlations among Physiological Variables in a study of air traffic controller workload modulation with variable number of aircraft. NA: number of aircraft; TLX: NASA self-report workload metric; Std SC: normalized skin conductance; Std SP: normalized skin potential; Std SBF: normalized capillary blood flow measured through the skin; Std ST: normalized skin temperature; Std IHR: normalized instantaneous heart rate. Bold values show significant correlation. SC, SBF, and IHR show significant correlation with changes in NA. Normalizations (standardizations) were performed against baseline data per subject to decrease inter-subject noise. 31 NA TLX Std SC Std SP Std SBF Std ST NA 1 TLX .98 1 p<.001 Std SC .93 .89 1 p=.002 p= .008 Std SP .77 .67 .91 1 NS NS p=.005 Std SBF - .97 -.94 -.87 -.7 5 1 p<.0001 p= .001 P=.02 NS Std ST - .79 - .80 -.62 -.43 .82 1 NS NS NS NS NS Std IHR .98 .95 .97 .85 -.93 -.88 p<.0001 p<.0001 p<.0001 p= .03 p=.002 p= .005 Adaptive automation (AA) is the rebalancing of workload between the computer and human . Low workload levels can be supplemented with usually routine tasks that will keep the operator attentive, while providing the subject with additional mission information . Th is "extra information" may not be critical, but it will keep the subject from disengaging from the overall task. The goal of AA is to maintain peak performance of the system, in the Al to A3 reg ions. Kaber studied AA in terms of a simulated ATC task in 2005. Forty non -professional participants were monitored for primary and a probe secondary task performances. Results showed that primary task performance was greatest when AA was added to the system. 34 UNCLASSIFIED/ }Fdk OFFICIAL 091!! 8HLY 14
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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.