{"id":32514,"date":"2024-01-25T14:40:31","date_gmt":"2024-01-25T14:40:31","guid":{"rendered":"https:\/\/one.sightlinemg.com\/c4isrnet\/uncategorized\/2024\/01\/25\/northrop-harnesses-machine-learning-to-aid-space-force-missile-parsing\/"},"modified":"2026-08-08T18:50:47","modified_gmt":"2026-08-08T18:50:47","slug":"northrop-harnesses-machine-learning-to-aid-space-force-missile-parsing","status":"publish","type":"post","link":"https:\/\/one.sightlinemg.com\/c4isrnet\/artificial-intelligence\/2024\/01\/25\/northrop-harnesses-machine-learning-to-aid-space-force-missile-parsing\/","title":{"rendered":"Northrop harnesses machine learning to aid Space Force missile parsing"},"content":{"rendered":"\n

WASHINGTON \u2014 Northrop Grumman is developing software it says can simplify the high-stakes process of discovering, classifying and monitoring missile launches across the globe by leaning on pattern-recognition capabilities.<\/p>\n\n\n\n

The defense company is in the process of refining what it calls False Track Reduction Using Machine Learning for the U.S. Space Force, with eyes on delivery in early 2025. It is anticipated for use in the Space-Based Infrared System<\/a> program, or SBIRS, and has potential application in other overhead persistent infrared assignments.<\/p>\n\n\n\n

Space Force personnel track thousands of potential missile incidents each month and must contend with false alarms. Increasingly delicate spying technologies, proliferating satellites, ever-evolving weapons and military flare-ups overseas can aggravate the already-complicated process.<\/p>\n\n\n\n

Northrop\u2019s offering is designed to ease the information avalanche<\/a> analysts face by parsing what may not be an actual launch or outbound projectile while, at the same time, ensuring no \u201creal event or real missile\u201d is improperly sorted, according to John Stengel, the director of the company\u2019s mission exploitation enterprise.<\/p>\n\n\n\n

\u201cAs sensors get better \u2014 as sensors in space improve \u2014 they get more sensitive. As sensors get more sensitive, the more false tracks we get,\u201d Stengel said in an interview with C4ISRNET. \u201cHaving the ability to leverage machine learning to help the human in the loop, so to speak, do his or her job is to become absolutely critical.\u201d<\/p>\n\n\n\t