Multidomain Observing and Orienting: ISR to Meet the Emerging Battlespace: Part II

 

Reading Time: 15 Minutes

By: Sean A. Atkins

Editor’s Note: Today we present part two of a two-part series. This part frameworks future effects intelligence, surveillance, and reconnaissance (ISR) enterprise and the relationship with multidomain operations. This article was originally published in the Air and Space Power Journal in Fall of 2018 in its entirety.

 

Implications for the ISR Enterprise

The evolving battlespace demands and observe/orient (OO) requirements outlined in part 1 of this artice build toward an inflection point for the intelligence, surveillance, and reconnaissance (ISR) enterprise. New multidomain challenges and opportunities are beginning to present themselves, but existing ISR tools, organizations, and concepts are not postured to engage them. The positive news is that new and developing ideas within industry and the ISR community provide a useful foundation to build from. Many of these ideas and tools emerging in pockets of innovation can be refocused and tied together to begin to meet the multidomain operations (MDO) challenge. Just as early aircraft changed how military forces observed their battlespace, providing awareness far beyond the perspectives of ground and naval forces, these new concepts and capabilities are putting an ISR paradigm shift in sight, one that can provide a more holistic understanding of the complex multidomain battlespace. It is a paradigm shift with, as the Air Force lead for intelligence analysis highlighted, broad implications for “what we collect, how we process it, how we analyze it, and how we connect to the operators, platforms and staffs that need that information”.

Rethink the Battlespace

First, it is essential to rethink the battlespace itself, re-conceptualizing it as a layered and interconnected multidomain maneuver-space. This interconnected continuum of domains contains innumerable new maneuver options that are not sufficiently captured through traditional, often stovepipe OO constructs. Within modern military operations exists a tight interdependence between individual domain functions. Being able to discern and visualize the layers, interconnection points, and dependencies will provide the sort of battlespace understanding that enables multidomain action.

Rethink Actors and Activities

To achieve success in a multidomain competition, ISR professionals must also rethink their conception of activities and actors within the battlespace. Instead of focusing on one dimensional targets with narrow activity sets, ISR must hunt targets as multidomain systems with exploitable interconnected surface area. Further, it must have a broad baseline understanding of the multidomain environment to detect anomalies and be able to observe and orient off the series of interconnected activities that relate to a particular behavior or actor. Most current ISR constructs stovepipe their questions and focus, narrowing collection and analysis, resulting in missed opportunities and vulnerabilities.

Recent developments in ISR methods and technology provide the practical foundation to realize this necessary perspective shift. The advancement of object-based intelligence (OBI) and activity-based intelligence (ABI) concepts, in which intelligence work is organized around the person, place, or thing being studied along with its associated activities vice any particular organization or collection system, enables the more holistic OO that MDO requires. Instead of interpreting a snapshot image to discern a narrow amount of information, an MDO ABI approach would focus on understanding what is happening with the person, place, or thing studied and how that activity and its interconnected elements and environment change over time. The ISR paradigm shifts from simply identifying enemy capabilities and estimating motivations, to assessing a changing battlespace and its impact on operations.

Change How We Observe the Battlespace

Decisions that drive MDO demand new information and awareness that necessitate a corresponding change in how we observe the battlespace. In order to quickly identify and leverage opportunity for cross-domain maneuver and effects, future ISR operations should involve collecting broader information across all domains. More specifically, MDO requires greater data volume, variety, and velocity derived from more sources. Increased interconnectivity between domains means actors and activities in one domain are more likely to appear with exploitable surface area in others. For example, during the 2014 Russian seizure of Crimea, the lack of traditional telltale signs of invasion surprised intelligence analysts. While Russian soldiers obfuscated their traditional visual and EMS signatures, where ISR was postured to look, they interestingly began showing up prominently in cyberspace on social media sites including Twitter, Instagram, and the Russian version of Facebook.

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Russian soldier Alexander Sotkin’s Instagram posts revealing clandestine movement into Ukraine

 

Of course, this kind of exposure is not limited to Russians in Crimea. Private citizens are publishing volumes of information revealing military activities, from spy ship tracking to missile launch details.

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Twitter feeds publishing locational data on military assets and activities

 

The power of these sources was demonstrated recently when amateur analysts published a minute-by-minute account of the combined US–UK–French strike on Syrian chemical weapons facilities as it was occurring. The details released via Twitter updates included tanker support tracking, strike aircraft routes, and ISR aircraft positions.

Further, developing the kind of awareness that enables quick multidomain action requires continuous collection that not only feeds characterization of actors and activities but of the multiple environments that make up the multidomain battlespace as well. Continuous sensing across domains enables quicker identification of multifaceted patterns and anomalies that lead to speedier identification of opportunities to exploit and vulnerabilities to address. Additionally, increasing data sources and types provide analysts the ability to correlate and cross-verify, ensuring increased veracity of conclusions. It also enables big data reliant methods such as OBI/ABI to perform better with increased volume and variety. As noted in the Joint Operational Access Concept (JOAC), this requirement of broader and continuous collection has implications for “steady state sizing, systemic capacity, and analytic technologies of intelligence forces”.

To accomplish this, the type of sensors employed and even what constitutes an ISR platform must fundamentally change. In contrast to ISR platforms equipped with narrowly focused sensor suites, observing for MDO requires sensor systems capable of collecting broader types of data. It also demands shifting to an “everything a sensor” model in which every asset, regardless of primary purpose, can simultaneously act as sensor platforms. Every friendly point of presence is also an access point into the battlespace that can be leveraged for collection and, if needed, as a pivot point for potential multidomain maneuver. As Gen Carlton Everhart highlighted during a discussion on air mobility assets, “we need our aircraft to be sensor platforms that can gather and securely communicate information”.

This does not mean scrapping the charge to develop ISR sensors and systems designed to penetrate and survive in high-threat areas. These are still critical to acquiring data that would be otherwise impossible to reach. The end result will look similar to a multidomain crowd-sensing effort similar to commercial products like Waze. Every platform and point of presence should be an ISR contributor, an element of a larger intelligence collection network composed of interlinked sensors across all domains.

Further, this approach to collection demands a more prominent role for opensource data. As Col Sean Larkin noted in Foreign Affairs, “over the next decade, the market-driven explosion of surveillance sensors and data analytics will bring an unprecedented level of transparency to global affairs. . . offering inexpensive and automated reports on everything from crop yields to military act”. Dr. Jon Kimminau describes how “the foundation of knowledge we need. . . can come from Open Source,” freeing more exquisite sensors to collect less accessible data”.

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The openly available LiveUAmap’s coverage of conflicts in Syria and Crimea produced information that often rivaled classified sources and methods. (Reprinted from image of map of Syria to illustrate unknown aircraft in News Live, accessed 14 May 2018, https://syria.liveuamap.com.)

Change how we derive understanding from observation

With new demands to understand more detail on more aspects of the battlespace and activities within it, the challenge then becomes deriving understanding from observation that produces vastly increased data velocity, variety, and volume. This challenge is at the heart of multidomain orienting and requires a significant shift in analysis to produce decision-level understanding without proliferating a multitude of systems that only bury users in data. Fortunately, this is another area where intelligence professionals can adapt recent initiatives in data analysis tools, technologies, and concepts.

First, the current DOD and broader intelligence community efforts to adopt a bigdata approach must be redoubled and steered to facilitate multi-domain awareness. Shifting to a big-data construct is ideally suited to the MDO challenge in that it is designed to derive deeper understanding in greater interconnected complexity with vast data volumes and types. As Dr. Kimminau again highlights, increasing data types and volumes should enable cross-domain thinking. In fact, even with “dirty” or raw unprocessed data, a common concern of many ISR professionals regarding big data, these new analytic approaches are proving able to better discern activities or opportunities that analysts did not know to look for in the first place.

Second, artificial intelligence (AI) and machine learning must be further invested in and integrated to provide the speed of analysis in complex interconnected environments to out-orient adversaries at the operational and tactical levels. The multidomain battlespace will increasingly overwhelm existing analytic approaches that primarily rely on human and “brute force” computer analysis. At the same time, advances in commercially developed AI, such as IBM’s Watson, are capable of leveraging vast data to learn and develop, as James R. Clapper described, “a beautiful intuition” that can identify and even predict the sort of opportunities and vulnerabilities that enable MDO.

Additionally, AI can further accelerate analysis by quickly translating raw or unstructured data into a more useable form. For instance, AI is proving increasingly proficient in deriving data within raw data, structuring it to become useable by follow-on analytics. A recent example that highlights the utility of these advances is found in a Google team’s research on Convolutional Neural Networks’ ability to learn, identify, and catalogue objects or activities in video and audio data. Quickly deriving and structuring useful data embedded within other data is critical to maximizing the possibility of finding multidomain opportunities and vulnerabilities, enabling tighter and truer orienting. As the previous Deputy Secretary of Defense noted, “the Department of Defense must integrate artificial intelligence and machine learning more effectively across operations to maintain advantages over increasingly capable adversaries and competitions”.

Change How Users Interact with the Observe and Orient System

Changing the OO paradigm and supporting system to enable MDO creates new opportunities for decision makers at all levels in how they engage that system. In particular, the technologically and conceptually complex system described above requires a new approach to crafting and translating critical intelligence requirements to drive collection and analysis. Further, decision makers at all levels will add to and shape the system in real-time as participants, not just receivers.

For this new system to perform, the ISR enterprise must build the connective tissue between decision makers’ information needs and the complex analytic system that supports them. This connective layer must perform dynamic mission data science (DMDS) to translate information requirements into analytic models and algorithms that can adapt to meet the demands of an evolving battlespace, enabling true multidomain awareness and prediction. To achieve this higher-order predictive analysis that tightens the OODA loop in multidomain complexity, there must be people in place who understand the requirements and how to dynamically craft the analytic tools to get there.

Further, the DMDS function must exist broadly across the operational force to enable multidomain action at all levels of decision and execution. The same data and analytic expertise that provides operational-level insight to JFCs can be leveraged to quickly identify or predict opportunities and vulnerabilities at the tactical level. Different algorithms can be crafted and run on the same data to serve different perspectives and needs. As Vice Adm Jan Tighe notes, it is critical to “more rapidly update, modernize, and customize our applications inside their actual environment with the end-user community fully embedded in that journey”. To achieve OODA advantage across a continuum of domains at each level, ISR data science functions must be embedded with each of these end user perspectives.

In addition to connecting with the OO system through a DMDS layer, decision makers and operators will also interact directly with the system to further orient and sharpen collection and analysis. In its simplest form, it is similar to how companies like Amazon leverage consumer interaction with their system to generate more data to analyze and determine how to shape what it produces to best fit the user’s needs. In this construct, decision makers are more than users of information, they are participants in the data analytics.

Change How We Architect and Evolve the ISR System

The system that begins to take shape in the descriptions above points toward a change in how the ISR enterprise is designed and, probably more importantly, how it is quickly evolved. The shift toward MDO is largely technology driven and, as such, advantage can be lost just as easily as it is won when adversaries integrate the next technological development that provides it an edge. Because the majority of information technology development is led by private industry, the US must reshape its acquisition model to enable broader and faster partnership with industry. The current infrastructure model and acquisition processes do not allow for the speed required to consistently evolve ahead of threats.

The future ISR infrastructure must be an open architecture system that maximizes interoperability between services and partners, as well as the ability to quickly integrate new capabilities from across industry. It must be based on the same common industry standards that allow the quick evolution and integration of new and disruptive technology in the commercial world. In a battlespace where speed and broad interoperability translate to significant advantage, proprietary developments by a handful of defense contractors is increasingly a national security liability.

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Leveraging Dynamic Mission Data Science to conduct multi-domain maneuver, enabling asymmetric advantage that outpaces adversary observe, orient, decide, and act capabilities

An open architecture platform makes it simple to agilely adapt and leverage new sensor or analytic advances as soon as the industry develops them, keeping the ISR enterprise on the technological edge at less cost. A competitive advantage in a complex multidomain battlespace will be achieved by whomever can first leverage developments that drive faster, more capable OO operations: machine learning, cloud analytics, human-machine teaming interfaces, supporting information infrastructure, and so forth.

Further, an open architecture makes possible the degree of interoperability required for interservice and interpartner effectiveness in a multidomain environment. The current architecture, built over decades of individual service initiatives that created proprietary products, hinders, or precludes interoperability between domain operators, and thus the true Joint operational flexibility required for multidomain advantage. As a recent C4ISR article describes, “the idea behind an open-systems architecture is to create opportunities where you don’t have stovepiped, proprietary systems that don’t allow for things to plug in”. An open architecture system ensures not only that the ISR enterprise can iterate with industry faster, but that it will more easily interconnect across all domain operators and international partners.

Success in a multidomain environment also depends on the ISR enterprise’s ability to eliminate stovepipes. At the very heart of the MDO concept is the need for quick maneuver or action between domains. The supporting OO system cannot have barriers in place that prevent or slow the identification of multidomain opportunities or vulnerabilities. The effectiveness of a big-data approach, for example, relies on its ability to leverage disparate multidomain data to correlate opportunities and build a more holistic awareness.

At the information infrastructure level, this means breaking down stovepipes between services and agencies, as well as the types of collection (signals, human, imagery, open source, and so forth). Currently, every type of intelligence is stovepiped, often with separate information environments, and even within each there exist sub-stovepipes of more specific types of collection. Breaking down these stovepipes is critical to transitioning to become data-focused and will require a reexamination of current classification, access, and data sharing protocols.

Change How We Organize to Orient

This re-examination also calls for a change in how the analytic force is organized, moving further toward a sensor agnostic, collaborative, and data science focused force. The goal is to move away from stovepiping thought or data access in a way that limits analysts’ ability to identify multidomain opportunity and vulnerability. For the DMDS layer described above to operate effectively, teams composed of analysts, data scientists, and programmers are required at each of the decision-making levels and perspectives. DMDS teams must be present at the unit level to develop and dynamically modify models and tools that feed tactical decisions for ground, air, space, cyber, and maritime operators. These teams must also be present at the JTF and component levels to develop and dynamically modify the models and tools that feed operational decision making. Further, this analytic force arrayed at various levels and perspectives should not be hindered by organizational boundaries to collaborate, enabling an adaptive approach based on a more open organizational construct.

Fortunately, if a cloud-based infrastructure that eliminates stovepipes and enables a true multidomain big-data approach is meaningfully implemented, there will not be a need to expand the ISR workforce. Currently, a majority of the ISR workforce is engaged in time-consuming data-processing functions. Leveraging AI and big-data analytics to increasingly conduct data processing functions potentially liberates thousands of minds to work on analytics. As Vice Admiral Tighe again points out, the Navy’s migration to cloud-based architectures, both ashore and afloat, will “enable analytic environments and battle management decision aids that reduce the dependency on our people for tasks that can be automated and free up our analysts to go further, faster in a human-machine teamed environment”.

Conclusion

The development and proliferation of advanced technology are once again changing the battlespace and shifting the character of conflict away from what the US military has prepared for. Still in development, the MDO concept proposes a better integration of capabilities across all maneuver domains to overcome challenges that increasingly defy current operational concepts. Although MDO is not a new idea, its emerging shape places new demands on the joint force that have fundamental implications for how it observes and orients itself. MDO will require re-conceptualizing the battlespace, how we derive understanding, reshaping approaches to constructing and organizing ISR, and new ways of using and interacting with the ISR enterprise.

More than 30 years ago, Boyd expressed the need to simultaneously “generate many different possibilities as well as rapidly implement and shift among them” to outmatch adversaries. The MDO concept is built on the idea that these possibilities are exponentially increasing in number as interconnectivity between domains, both physical and virtual, continues to grow. Without the ability to observe and orient to these new combinations of possibilities, however, MDO will remain out of reach. Just as ISR shapes and drives decisions and actions, ISR professionals are now in a position to develop a multidomain OO construct that shapes and drives multidomain warfare from concept to practice.

 

Sean A. Atkins is a doctoral student in the Security Studies Program at the Massachusetts Institute of Technology. Previously, he was the deputy director of future warfare concepts and an instructor in the Air Command and Staff College’s multidomain operations and strategy program. Sean has served in a range of assignments from forward operating bases in Iraq to the Office of the Secretary of Defense. He is also the founding editor of Over the Horizon.

 

Disclaimer: The views expressed are those of the author and do not necessarily reflect the official policy or position of the Department of the Air Force or the U.S. Government.

 

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