By Kris Osborn, President, Center for Military Modernization
(Washington D.C.) Massive amounts of satellite images, video and data flow in at unprecedented speeds, EO/IR sensors on fighter jets generate target specifics, ground-based air-defense radar “lights up” enemy aircraft and both air and ground unmanned systems collect, analyze and transmit time-sensitive data. Meanwhile, dismounted soldiers and ship commanders consistently uncover new threat information and need to “network” key location detail, intelligence data and targeting specifics to an optimal “shooter” or weapons platform in a position to attack.
How are all of these vast pools of seemingly disparate sources of incoming sensor data, arriving through different transport layer communications technologies, collected, processed, analyzed, integrated and quickly streamlined to combat decision-makers under enemy fire? Perhaps one data stream is an rf signal, another GPS while a third comes from a separate digital datalink or wireless signal? How can interwoven data from otherwise distinctly different data formats be analyzed in relation to one another in time to destroy an enemy?
Warfare is becoming increasingly “hyperactive,” “multi-domain” and moving at lightning speed as forces seek to find, identify, pinpoint and destroy targets much faster and at longer ranges than the enemy.
Making this happen or, “fighting at the speed of relevance,” as US Military leaders like to say, is ultimately determined by the force which better uses “information” as a weapon of war and operates faster than or inside of an enemy’s decision-making cycle. High-speed, AI and machine-learning-enabled computing and data processing, particularly if fortified by gateway technologies able to “translate” and “integrate” otherwise incompatible data formats, can expedite this process in unprecedented ways.
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Operating within this conceptual framework, various weapons developers and industry innovators are working to “break ground” on new paradigm-changing technologies which can massively “speed up” attack speeds in warfare. Arlington, Virginia-based technology firm Royce Geo, for example, uses AI-enabled algorithms, machine learning applications, computer automation and gateways to “translate” and “integrate” data streams and expand the application of its now operational cloud-enabled analytics and data-modeling technology known as the CURVE Operating Environment (OE). CURVE OE enables users to gather, merge, analyze and help transmit precise, time-sensitive combat data from both commercial and military platforms.
Furthermore, CURVE OE is a commercial solution that delivers the capability to automate and scale advanced analytics to make sense of disparate geospatial, signal, human, open source, and cyber intelligence data streams to support national security.
CURVE is a methodology that was developed into software and deployed to automate the intelligence process. It stands for Coordinate, Understand, Resource, Visualize, Enhance.
Royce Geo delivers CURVE OE and data services in a data/ software-as-a-service model, to support the automation of the intelligence lifecycle, developers explain. The CURVE OE is currently providing operational support to the IC and DoD, both conventional and SOF units today.
“Over the last several plus months, we built an environment where we bring in disparate data sources, analyze the data, and input them into a database to be visualized in a common operating picture for the end user to make sense of,” Adam Estrada, CTO, Royce Geo, told Warrior in an interview.
This common operational picture comes to life due to data scaling, organization and processing. One of the foundational elements of tactical ISR in warfare is described as PED, for Processing, Exploitation & Dissemination, a method of sifting through vast volumes of data to pinpoint moments or objects of relevance, distinguish and identify them and quickly pass only the “scaled” or “needed” data to human decision makers. This process has evolved substantially over the years to involve much wider spheres of information, AI-enabled, high-speed computing and, as in the case of Royce Geo, machine-to-machine automation.
“Using artificial intelligence and machine learning, CURVE coordinates, understands, resources, and visualizes millions of data points to enable leaders to make informed decisions and initiate actions on the battlefield in seconds,” according to Royce Geo’s website.
“What we really focus on are mission requirements. Our charter is not to just build software, it’s to build software, based on what the requirements of the end user are. So as far as automation goes, we’ve got a series of capabilities that follow the no code paradigm of drag and drop components into a directed graph,” Estrada explained.
While already supporting key elements of government such as the intelligence community and security agencies, Royce Geo is now immersed in conversations with the larger US military services to explore broader applications of CURVE OE.
“Many groups within the IC are familiar with commercial data and have been using it for years. However, what we were seeing within our Military, particularly the Army, was a bit different. They knew of the data, but haven’t been able to fully harness it at scale to support their larger collection posture; all Warfighting functions. The good news is that there is a way to solve this issue and we have that solution,” Adam Ashurst, Director of Operations, Royce Geo, told Warrior in an interview.
CURVE could to a certain extent be described as a breakthrough, cutting-edge AI-enabled PED technology which not only uses cloud-enabled systems to gather, organize and streamline seemingly disaggregated or disconnected pools of data but also relies upon machine learning and computer automation to perform time-sensitive analysis and data-processing exponentially faster than a human could.
“We’ve been very successful in stitching data sources together that are either too big to fit into an Excel spreadsheet or don’t have an end user with the knowledge of how to interpret and interrogate a large data set. So when you think of a data workflow, that’s our specialty,” Ashurst explained.
Interestingly, the technological and tactical dimensions associated with CURVE seem to align closely with current Pentagon efforts to exact a new kind of information-driven, joint, “multi-domain” synergy between air, sea, land and even undersea domains. The Army’s Project Convergence, for instance, has for several years now used AI-enabled computing to gather, process and transmit critical target information from forward operating mini-drones to larger drones, air platforms and ground control centers wherein advanced AI-empowered systems organize data, bounce it off of a seemingly limitless database of facts, historical scenarios, variables and analytical programs to essentially “optimize” sensor to shooter pairing by identifying the best “effector” or weapons system for a particular threat scenario. While humans are making decisions regarding lethal force, machine-to-machine connectivity performs the data analysis and transmission to organize and streamline massive amounts of operationally critical data.
“We built an extremely robust analytics and data modeling capability primarily working the “tip and cue and orchestration of space-based capabilities.” So when an event happens in a specific location, we capture that information and can task a collector to go look at that in a machine-driven manner,” Ashurst said.
Satellites, drones, ground centers and armored combat vehicles can all share streamlined, organized ta
rget specifics informing the human decision maker who can quickly learn which method of attack is best suited for a given enemy target. Surface unmanned vessels, warships and Navy aircraft are similarly linked in a comparable Navy program called Project Overmatch. The concept is similar, meaning interfaces merging otherwise incompatible transport layer communications systems are integrated through an AI-enabled gateway system of interfaces to gather, process, organize and share information between surface, air and even undersea nodes. The Air Force has something similar, called Advanced Battle Management System (ABMS) which seeks to generate a meshed series of combat nodes connecting aircraft with one another to, in similar fashion, expedite and optimize speed of attack. This Air Force process, which progressed in terms of “on-ramps” was at several points able to identify critical target information. In one instance during ABMS “on-ramp” experiments unmanned aerial systems were able to send targeting specifics to cue ground-fired artillery. This is precisely the kind of joint synergy informing the Pentagon’s fast-emerging Joint All Domain Command and Control (JADC2) program.
Much like JADC2s integration of progress from the Army’s Project Convergence, Air Force ABMS and Navy Project Overmatch, CURVE seems positioned to expedite the process of using “information” as its own high-speed weapon of war to stay in front of an enemy and prevail in high-speed, multi-domain warfare. Building upon this perhaps if applied more broadly across the military services, CURVE could succeed in integrating previously separate commercial technologies and streamline data analysis using its cloud-enabled environment.
A big thrust of Royce Geo’s efforts involves integrating commercial and military technologies in a way that expedites critical data transmission yet ensures continued security and the requisite amount of data scaling. For instance, Estrada explained that CURVE can synchronize commercial video feeds with a SAR (Synthetic Aperture Radar) with advanced analytics and government systems to ensure streamlined, processed seamless connectivity across a vast joint warfare environment.
“We’re looking at how we take this platform and run it not only in a purely commercial manner but how we take the very valuable commercial data intelligence and put it into the Intel environments to help them make better sense of large disparate, both commercial and national data,” Estrada said.
CURVE developers in part describe this in terms of a “man-out-of-the-loop” process wherein machines can gather, process and exchange data themselves to increase efficiency, save time and expedite functions that could otherwise be too time-consuming for humans to perform.
Further refining CURVE for broader operational use requires continued hardening of data networks and solidifying “reliability” within and between data systems. An AI system can only be as effective as its database, so information must be properly added, tagged and scaled appropriately to specific mission needs. What happens when an AI-capable database encounters incoming information that is not part of its database? Is it able to perform analysis based on a host of interwoven variables and essential “learn” or “discern” things not previously identified? This is the cutting edge of AI, often referred to as “zero trust,” referring to advanced technology able to increase accuracy and reliability of analysis empowered by AI-capable algorithms. CURVE might prove advantageous to this process should it be able to integrate newer, emerging commercial technologies into an integrated system with military networks, all while working to ensure increased security and reliability.
Kris Osborn is the Military Affairs Editor of 19 FortyFive and President of Warrior Maven – Center for Military Modernization. Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army—Acquisition, Logistics & Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel, and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.
