US Army developing faster data kill chain for lethal and non-lethal fires
The U.S. Army is putting new emphasis on using technology, including artificial intelligence and machine learning, to speed up the “kill chain” process of locating and firing on targets, said a panel of experts at Technical Exchange Meeting 9, or TEM 9, a gathering of industry and military experts. Doug Graham, Network CFT, elaborates.
Follow Army Recognition on Google News at this link
The U.S. Army is putting new emphasis on using technology, including artificial intelligence and machine learning, to speed up the “kill chain” process of locating and firing on targets (Picture source: U.S. DoD)
The TEM (Technical Exchange Meeting), held in Nashville in December 2022, is the 9th in a series of sessions focused on informing industry to help shape the Army’s communications and data systems for the battlefield of the future.
Army experts said one of the challenges in modernizing the kill chain process is the increasing amount of data Army leaders receive due to the increased use of sensors and communications systems on satellites, aircraft, drones, vehicles and even soldiers. Undersecretary of the Army Gabe Camarillo noted in opening remarks of the two-day meeting that the challenge is often not a matter of too little data, but “too much.”
Camarillo and many of the experts speaking at the TEM 9 event said artificial intelligence and machine learning — AI and ML, respectively — technology can help distill that tsunami of information to make it far more concise and useful. Coupling these technologies with a modernized unified communications and data network will enable commanders to make well-informed decisions far more quickly than their opponents. This is called decision dominance.
Col. Matt Paul, Project Manager of Mission Command, part of the Program Executive Office Command, Control, Communications-Tactical, said AI and ML tools can assist in processing the flow of battlefield information to quickly locate and identify targets and then help commanders select the best assets for engaging the target. These assets include direct-fire weapons, artillery, mortars, aircraft, missiles and even directed energy weapons such as lasers. The kill chain construct can also help the Army decide more quickly on when, where, and how it can engage targets using lethal and non-lethal weapons, Paul said.
Col. Rory Crooks, director of the Long Range Precision Fires Cross-Functional Team, described several efforts aimed at modernizing the kill chain from the strategic level to the edge of battle.
Efforts to modernize the kill chain start with paring down available data to only the information that is relevant to solving problems at echelons like corps, divisions and brigades, Crooks said. Mission command implies that each commander at his echelon has a distinct mission, with a smaller subset of relevant information relative to all the information that is available, he explained.
The Tactical Intelligence Targeting Access Node, or TITAN, program, for example, processes all available intelligence to assist in targeting — finding the most important targets to a commander at echelon, he said. Other examples include the Joint Targeting Command and Coordination System, which will replace the legacy Joint Automated Deep Operations Coordination System, and new versions of the Army’s Advanced Field Artillery Tactical Data System, which will help pair the right joint and Army organic “shooters” beyond direct fire range at echelons above brigade. Additionally, Fires Synchronization to Optimize Responses in Multi-Domain Operations is a system at the brigade level and below that will pair sensors in the close fight with the best direct and indirect fire systems available.
As the TITAN program seeks to establish access to High Altitude and space-based sensor systems that can provide “deep sensing” far behind the line of battle, it will enable more effective use of long-range precision fires, said Col. Chris Anderson, Project Manager of Intelligence Systems and Analytics, which is part of Program Executive Office Intelligence Electronic Warfare and Sensors (PEO IEW&S). TITAN will also allow the use of input from multiple sensors from multiple locations, he added.
To test, develop and evaluate new sensor-related “kill chain” innovations involving AI and ML, PEO IEW&S recently launched “Project Linchpin,” Anderson said. This program is developing the Army’s first sensor-focused intelligence and machine learning operations pipeline to standardize and speed up the integration, delivery and training of AI and ML capabilities, he said. It will allow the Army to “train” AI and ML models to detect objects and alert analysts to possible targets. This can close the gap between the amount of raw data the Army processes, and the amount and kind of information commanders and decision-makers need on the battlefield.
Army Futures Command’s Artificial Intelligence Integration Center, DEVCOM’s Army Research Laboratory and PEO IEW&S are collaborating on Project Linchpin, which is focused on the Army’s newest sensor systems, including satellites, optics, electronic signals receivers and other advanced sensing systems.
“Our guiding principle is: models and algorithms are the fuel and ammunition for Artificial Intelligence,” Anderson said. “So we have to ask how we can rapidly and continuously deliver those algorithms to the platforms that need them, and then create a feedback loop so we can keep retraining those models.”
The effort to develop new sensor technology, new methods for processing the data, and building a resilient, secure and fast network continues to keep pace with other advancements, said Col. Paul, of PM Mission Command.
“The Army over the past several years has invested quite a bit in new long-range precision fires capabilities … new rockets, artillery and missile systems,” he said. “We have to support that with capabilities such as sensor processing, and digital fire support command and control. We need these processes to be more automated and more agile, intuitive and data-centric for the future.”