Editor’s Note: U.S. Air Force Col. Jason M. Brown is commander of the 693rd ISR Group, and Lt. Col. David Vernal is commander of the 450th Intelligence Squadron. Guest posts are intended to foster discussion, and do not represent the official position of USGIF or trajectory magazine.

Despite the continuing drawdown in Afghanistan, events of the past year prove threats to American interests and citizens abroad are not diminishing. To the contrary, we are living in one of the more dangerous, unstable, and amorphous strategic environments in the last 30 years. Many of the more recent crises—such as the simultaneous conflicts in Syria and Iraq, instability in Libya, the Ukraine-Russia crisis, Ebola outbreak in West Africa, and ethnic strife in South Sudan—highlight that more often than not the United States will not have a substantial ground presence or reliable partner to provide decision-makers with detailed information to quickly assess a given situation and chart a corresponding course of action. The U.S. government relies upon its Intelligence Community (IC) to provide insight into these emergent crises; in turn, the alarming speed at which these crises can develop and unravel demands intelligence organizations find new ways to quickly answer the “who, what, where, when, why, how, and what next?” questions.

As a member of the IC, the U.S. Air Force supports national decision-makers and joint commanders through global intelligence, surveillance, and reconnaissance (ISR), one of the service’s five integrated and interdependent core missions. Driven by ISR’s salience to the conflicts of the past decade, the Air Force relies upon the Distributed Common Ground System (DCGS) to rapidly access and analyze information collected by airborne sensors in real time. At the same time, IC-developed technology is now delivering on the promise of nearly instantaneous access to much of the community’s collective data through a world of data clouds, metadata tagging, and apps. National agency databases, especially those developed and sustained by the Combat Support Agencies, are becoming increasingly accessible. Customized IC tools that extract and manipulate cloud-based data are growing in power, sophistication, and flexibility. These capabilities are not reserved for Beltway analysts; they are also available to Airmen throughout the Air Force’s DCGS network that are actively modifying and improving these apps for broader use across the IC.

This access to troves of IC data allows DCGS analysts to fuse intelligence in proximity to the point of collection. Consequently, analysts can now meld new information with baseline knowledge in seconds and minutes, instead of what had taken hours and days. This analytic capability, forever sought but perpetually elusive, is rapidly becoming a reality. As such, the capability demands characterization to distinguish it from the forms of analysis long practiced by various elements of the IC. Because its defining aspects are the speed at which it seeks to meld new information with existing data, as well as its use of multiple intelligence disciplines close to the point of collection, we call this analytic capability “time-dominant fusion.” This article defines time-dominant fusion and its interdependent relationship with airborne ISR capabilities and Air Force DCGS.


At its heart, time-dominant fusion is a tradecraft focused on rapid discovery by correlating what is new with what is known. Put another way, time-dominant fusion focuses on the “who, what, where, and when” intelligence questions by discovering and describing events, actions, or state changes in an environment of concern. DCGS analysts are using powerful data forensic and workflow tools such as the Joint Enterprise for Modeling and Analytics (JEMA) to correlate, refine, and visualize large amounts of IC data collected by various sources. For example, analysts are automatically filtering and merging intelligence data from multiple disciplines to display results through a geospatial visualizer such as Google Earth. These rapid workflows enable analysts to quickly identify new locations for imagery collection to discern adversary presence and activity. This is in many ways similar to the IC’s emerging activity-based intelligence (ABI) methodology. Although the concept is still evolving, common descriptions of ABI also invoke a multi-discipline, Big Data-enabled approach to drive analysis and collection against previously unknown activity that are revealed through telltale signatures in correlated data. As the definition solidifies through experience, time-dominant fusion may prove to be a subset of ABI—albeit one distinguished by a purposeful and continuous integration of collection and analysis tradecraft, and linked specifically to airborne ISR operations. 

Time-dominant fusion proved crucial during the past year of emergent crises in Africa and the Middle East. Clashes threatened U.S. interests and citizens in places where the IC did not have widespread collection and analysis due to competing higher priorities. In response, national leaders dispatched airborne ISR to provide situational assessments and to help make critical decisions such as whether to evacuate American citizens and lock down U.S. embassies. The rapidly unfolding, chaotic situations demanded rapid discovery of facts on the ground: who the actors were; where they were located and moving; and where and when the violence could threaten U.S. interests or citizens. In these situations, decision-makers turned to Airmen to provide the knowledge needed for decision advantage, i.e., avoiding surprise, adapting to the situation, and influencing outcomes.

In the past, Air Force DCGS analysts processing, exploiting, and disseminating intelligence from airborne collection had limited access to any intelligence other than that derived directly from one or two airborne platforms and associated sensors. Traditionally, centralized IC nodes, such as a combatant command Joint Intelligence Operations Centers (JIOC), or national agencies needed to aggregate airborne collection with a variety of other intelligence disciplines to generate all-source analysis. However, while these organizations may have had access to myriad data sources, the analysts would not have had real-time access to complete sensor information, instead receiving only data-based products with the commensurate time delays. Today, DCGS analysts have access to much of the data formerly available only to theater or national organizations. Analysts no longer have to wait for data to come back to them as part of a fused all-source product to more fully understand the context and significance of observed activity. Instead, DCGS analysts can now participate fully in the discovery of critical information. Throughout the recent crises mentioned, this often involved relying upon time-dominant fusion to rapidly identify the presence and identities of armed parties, assess situations in near-real-time, and drive additional collection opportunities based on the agility allowed by quickly melding all available intelligence sources.

In short, the IC’s cloud technology now allows fusion far closer to airborne ISR sensors than ever before, such that real-time collection can be integrated with archived information to further adjust collection on the fly—generating a rapid decision cycle that will be far more fruitful than simply “flying the collection deck” and analyzing only what was produced. In contrast, much of the IC’s traditional, content-driven, expository analysis places less emphasis on rapidity or the data source and greater emphasis on the analytic depth and explanatory narratives needed to understand individuals’ actions, intent, and capabilities.

It is logical to ask whether time-dominant fusion might duplicate existing functions within the IC. Fusion performed close to the point of collection does not preempt multi-source analysis performed elsewhere, but is instead a natural evolution driven by technology and new processes that create capabilities. As an analogy, web-enabled services replaced older technologies associated with organizing and accessing data, but few would argue these new capabilities duplicate prior services. Google and Wikipedia, for example, are continuously displacing hundreds of services including phone books, gazetteers, and hardbound encyclopedias, because those publications could not offer on-demand access to their content, which was subject to increasing obsolescence the moment they were printed. On the other hand, there remains an insatiable thirst for references that focus on providing deeper context to what was already generated. In today’s ever-changing information environment, our ability to balance short bursts of immediately-accessible information with sources offering deeper content yields new and exciting possibilities in terms of generating actionable intelligence. Many of us now balk at planning a full day of activities prior to leaving home, instead expecting decision-quality data to be available via mobile devices as we adjust plans on the fly. At the same time, we still value reliable, high-quality information, accessing individual or crowdsourced expertise to aid our decision-making throughout the day. Fundamentally, modern technology can make us more agile, nimble, and adaptive, but does not obviate the need for the deep content required to help generate rich contextual understanding of the environment.

We stipulate, therefore, that intelligence organizations performing time-dominant fusion cannot replicate the capabilities of intelligence organizations postured to focus on the more complex intelligence questions: the “why, how, and what next.” Sensor-proximate fusion does not replace traditional in-depth, all-source analysis performed at organizations and agencies such as JIOCs or “three-letter” national agencies, just as these organizations cannot replicate the conditions necessary for time-dominant fusion—largely because of architecture limitations and policies on disseminating “raw” collection. Rather, time-dominant fusion and content-driven analysis are mutually supporting, rather than mutually exclusive. They require close collaboration between organizations specializing in one form of analysis or the other to effectively translate a decision-maker’s intelligence problems into a focused and flexible ISR strategy. Working together, organizations both upstream (close to the sensor) and downstream (close to decision- and policy-makers) can iteratively evolve strategies to develop and build the intelligence necessary for military and national leaders.

To place time-dominant fusion in the proper context, we propose categorizing the roles of IC organizations according to their echelon (i.e., whom are they charged to support?) and by the capabilities and limitations of their physical locations and virtual positions within the IC networks (upstream or downstream). Connectivity, data flow, the skills of the assigned workforce, authorities to access certain types of intelligence, and organizational links all matter. Moreover, their complex interactions are likely to frustrate any intent to build simple models defining activities that various intelligence organizations should or should not perform. This is especially true as U.S. reactions to world events and complex adaptive adversaries generate unexpected combinations of the aforementioned factors, such that the analytic task organization will look different, for example, in a crisis involving localized protection of American citizens, compared to one involving wide-ranging offensive combat operations. 

The explosive growth of IC information networks means the path from collection to a decision-maker can be unique for each new piece of data, and rigid distinctions between collection, processing, exploitation, analysis, and production are almost meaningless in some cases. Rather, roles should derive from the optimal combination of personnel and capabilities to deliver the right information to the right customer at the right time. This is the fundamental goal and purpose of time-dominant fusion. When employed properly, it becomes a vital capability for military commanders and the IC. Time-dominant fusion will be enormously beneficial in terms of integrating DCGS, Air and Space Operations Centers (AOCs), and JIOCs—enabling environment characterization, threat warning, targeting, operations assessment, and other intelligence functions that are critical to joint operations.

In today’s information environment no single organization will own the monopoly on analysis, and analysis must no longer be viewed as a monolithic activity achievable only downstream from the point of collection. Even with access to common databases, different intelligence organizations will continue to have comparative advantages and disadvantages. Some will continue to rely upon renowned subject matter technical experts, while others will have the best access to intelligence end users such as joint force commanders and national decision-makers. Those with the best access to the newest information and closest to the point of collection, such as Air Force DCGS, will be poised to keep pace with dynamic events while drawing upon and directing information from and to other military and IC organizations.  


As an operational and organizational concept, time-dominant fusion is inextricably linked with airborne ISR, taking advantage of the collectors’ flexibility, range, and speed. The Air Force designed, procured, and sustained ISR capabilities—such as the U-2, RQ-4 Global Hawk, E-8 Joint STARS, RC-135 Rivet Joint, MQ-1 Predators, and MQ-9 Reapers—to address intelligence needs from national leadership all the way down to individual Airmen, Sailors, Soldiers, Marines, and Coast Guardsmen. While the airborne platforms and sensors are important, the real strength of DCGS resides in the multi-discipline analysts and operations sites that comprise the weapons system.

In the past 20 years, Air Force DCGS evolved from a forward-deployed, line-of-sight intelligence element exploiting and fusing U-2 imagery and signals collection, to a task-organized, reach-back weapon system generating intelligence from thousands of global airborne ISR sorties every year. The original formal Air Force DCGS requirements document outlined the need for the weapon system to include data fusion technologies to correlate multiple sources of ISR, and for assigned personnel to use fusion tactics to optimize the operational utility of those capabilities. Consequently in 2007, Air Force DCGS units created fusion cells to integrate data from multiple sensors. These DCGS Analysis and Reporting Teams (DARTs), immediately proved their worth because of the analysts’ ability to integrate and fuse airborne collection with multiple sources, while collaborating with supported end users in Iraq and Afghanistan. Since DCGS personnel work with raw data across multiple intelligence disciplines, they can meld different intelligence streams at their source rather than merely welding finished products much farther down the production stream—a process that is sub-optimized and inefficient.

When national or theater leaders task airborne ISR today, it is increasingly in response to dynamic, chaotic, rapidly-evolving situations. Employing these assets, especially in new areas without a substantial presence on the ground, suggests a national importance that obligates intelligence professionals to maximize every collection opportunity. Time-dominant fusion is an evolutionary step that can dramatically increase the value of every ISR sortie by relying on fusion activities to adjust missions dynamically, adapting in real time and shortening decision cycles rather than adhering to rigid practices that are often both inefficient and ineffective. 

To take advantage of the increasing importance and rapid evolution of airborne ISR and DCGS capabilities to perform time-dominant fusion, we recommend four key focus areas for the Air Force and IC:

Adopt a problem-centric approach to intelligence needs: To empower analysts at every echelon, leaders should articulate the problems they need intelligence to solve through straightforward narratives, instead of allowing staffs to disaggregate the complexity of an intelligence problem into discrete requirements that often mistake method for solution. A succinct problem statement derived in a collaborative venue focusing on ISR strategy can be the framework used to empower analysts at all levels by enabling a mission command approach to ISR operations. ISR professionals at every level will use the stated problem as the basis for collaborating, as well as developing and refining ISR strategies and workflows. Problem-framing steps inform judgment as new facts come to light, as new collection opportunities of potentially greater value present themselves, or as the situation evolves.

ISR strategies over collection management: Using a problem-centric approach, ISR professionals should organize for both time-dominant fusion and traditional analysis by arranging capabilities in time, space, and purpose as part of a broader, coherent ISR strategy. Current hierarchical collection management processes separate the tasks of collectors, exploiters, and analysts into ever-smaller discrete tasks, but in practice their reassembly downstream rarely works as elegantly as doctrine suggests. This Industrial Age mentality assumes the end goal is “finished intelligence” produced in centralized factories assembling components created in isolation from one another. Not only does this model mandate stove-piping, it ignores the temporal, bandwidth, and capacity costs inherent in centralization. Current processes that funnel collection requirements to a higher headquarters for methodical prioritization are inherently flawed, in light of the time it takes to adjudicate and order competing priorities and to direct lower echelons to execute the items on the structured laundry list. The dynamic information environment we live in and the adaptive adversaries we face render moot any strategy that depends on lengthy sequential processing—requirements may be obsolete before the first collection ever takes place. Development of problem-based ISR strategies elevates solving problems over satisfying requirements, which in turn allows analysts to precisely guide collection through continuous fusion, even under the stress of constantly changing circumstances.

Embracing a maker ethos toward Intelligence Community tools: Time-dominant fusion lends itself to the independent, tech-centric inventing and designing culture (known as the maker movement) familiar to the IC’s young analysts. The IC’s strategic investments in Big Data, a common information technology enterprise, and application-based analytics tools are already showing a substantial return on investment. DCGS analysts, with enthusiastic cooperation from the IC program offices, are designing rapid workflows using these tools to perform innovative “Big Data” analytics and integrate disparate data sets. Using tailorable software combined with their own impressive innovative solutions, junior analysts are shifting from time-consuming manual processes to automated workflows. In one case, using JEMA cut 95 percent off a group of DCGS analysts’ research time. Once designed and built, these applications and workflow improvements spread rapidly throughout the enterprise. Maintaining an ecosystem that reinforces analyst and organizational flexibility in building new tools to solve new problems will remain crucial to improving time-dominant fusion.

Intelligence services over intelligence products: In our experience, time-dominant fusion lends itself to graphical and textual displays organized geographically with a capability for displaying and highlighting changes over time. These displays use web and cloud technologies to incorporate dynamic updates and broadcast an intuitive intelligence picture to a wide user base. This mode, more akin to providing a service than a finished product, introduces exciting new possibilities for delivering the right information to the right user at the right time. For organizations performing time-dominant fusion, these displays are crucial to driving dynamic collection. Because they can interact, explore, and experiment with data in real time, intelligence professionals and their customers can apply these services in novel ways no static process has ever been able to support—such as displaying continuously updated adversary threat locations. 


Time-dominant fusion is a form of analysis performed as close as possible to the point of collection, designed to rapidly assess what is new, different, or unaccounted for in an area of interest by discovering and incorporating new information with existing baseline knowledge. Time-dominant fusion is an analytic and operational tradecraft that centers around three problem-centric tasks: arranging capabilities in time, space, and purpose; integrating organizations, sensors, and data; and displaying intelligence temporally and geographically. Successfully applying this tradecraft requires close problem-centric collaboration between DCGS units, AOCs, JIOCs, services, and national intelligence organizations and combat support agencies.

Time-dominant fusion is not novel; what now allows us to move from theory to practical application, however, is the relatively recent explosion in technology centered on the information environment and data accessibility. Technological growth, in conjunction with enhanced partnerships between national agencies and DCGS, is making time-dominant fusion from airborne ISR platforms and sensors more feasible than ever before. Commanders and other decision-makers are now able to rely upon analytic partners upstream to solve intelligence problems and deliver decision-quality information, rather than waiting for the intelligence factory assembly line to build finished products. The emerging tradecraft behind time-dominant fusion will enable decision advantage in unprecedented ways. Fundamentally, time-dominant fusion allows the joint force to extract the maximum possible data and intelligence from every single airborne ISR sortie. Doing so creates an exponential increase in the value of these sorties, compared to those driven strictly by a traditional collection requirements management process. In short, time-dominant fusion enables decision advantage through precision-guided collection.

Time-dominant fusion is not a panacea, and will not solve complex intelligence problems for which it is ill-suited—airborne collection cannot monitor illicit financial transactions or establish rapport with a well-placed source. However, insofar as our nation will continue to rely on our asymmetrical advantage of airborne ISR, we must pursue time-dominant fusion. This requires re-inventing organizational structures and intelligence workflows to fully exploit the IC cloud and adaptively task-organize to optimize sensors and comparative organizational advantages. Time-dominant fusion may take advantage of technology, but its real benefit lies in enabling front-line analysts to become collaborative, creative problem-solvers invested in the success of each ISR mission. 

Photo Credit: U.S. Air Force


Posted by Col. Jason M. Brown and Lt. Col. David Vernal

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