Published in Military & Aerospace Electronics
Written by Jamie Whitney
Military threats are accelerating at machine speed, so military forces are adding artificial intelligence (AI) and machine learning to their arsenals of sensor, signal, and image processing to analyze vast streams of data in real time. By pushing computing power to the tactical edge in aircraft, armored vehicles, and even soldier-deployed systems, AI-driven systems minimize decision-making delays and enhance situational awareness. Coupled with distributed processing architectures, these technologies allow for autonomous platforms and crewed-uncrewed teams to act with greater speed, flexibility, and resilience in contested environments.
Adopting AI-driven sensor, signal, and image processing at the edge enhances military operations by reducing reliance on centralized command hubs and minimizing the effects of network latency or signal interference in contested environments. By decentralizing computational workloads across interconnected platforms, forces can analyze and respond to critical intelligence at the source -- whether it's an uncrewed aerial system (UAS) detecting threats in real-time or a ground vehicle processing electronic warfare (EW) signals on the move. As defense organizations embrace these advancements, they speed autonomous decision-making, and strengthen mission effectiveness across land, sea, air, space, and cyber domains.
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Denis Smetana, senior product manager at Curtiss-Wright Defense Solutions in Ashburn, Va., says "Any time you are trying to combine data from different sources you need to normalize the data so that sampling rates, resolution, data characteristics, etc. can be combined together in meaningful ways. Otherwise, a difference in the definition of the data can cause distorted results."
