In this whitepaper, Overwatch Imaging outlines how AI-enabled sensor autonomy can reduce operator workload in airborne Intelligence, Surveillance, and Reconnaissance (ISR) operations. Learn more >>
The whitepaper examines the company’s TRL 9 Automated Sensor Operator (ASO), which automates sensor steering, wide-area search, object detection and tracking, and reporting, converting sensor data into structured, geolocated intelligence.
ASO has been integrated and demonstrated with EO/IR gimbals from Teledyne FLIR, L3Harris WESCAM, Trakka, and Trillium Engineering, and supports standards including NMEA 0183, STANAG 4609, and STANAG 4607. Edge-based processing enables AI inference and sensor-control logic to run locally, without requiring a datalink or ground-based processing for core operation.
Applications span maritime and tactical ISR, border and coastal surveillance, distributed uncrewed ISR, multipoint overwatch, rapid mapping, wildfire response, and infrastructure monitoring. Across fielded deployments, the whitepaper reports detection speeds four to ten times faster and effective area coverage two to 20 times greater than manually operated, non-ASO-equipped EO/IR gimbal systems using the same class of sensor.
To read the full whitepaper, visit the Overwatch Imaging website.
