
What happened
Teledyne FLIR OEM launched Prism A-ISR on October 6, software that uses AI to detect, classify and track multiple ground objects in real time from UAS and other airborne ISR platforms.
Why it matters
The module performs much of its processing at the edge, so airborne systems can analyze sensor data without relying on remote computing resources, the company says.
What to watch
The technology targets persistent surveillance, force protection and other defense missions, and Teledyne FLIR will demonstrate Prism A-ISR at the AUSA Annual Meeting in Washington, D.C., October 12–14, 2026.
WHO IT HITSDevelopers building airborne surveillance and defense platforms, particularly those integrating UAS with embedded AI hardware from NVIDIA and Qualcomm, gain a new software option that can be paired with Teledyne FLIR's NDAA-compliant Boson+ and Neutrino thermal cameras.
Summaries like this, in your inbox every morning.
Teledyne FLIR OEM is extending its Prism software portfolio to airborne platforms. Existing Prism products include Prism C-UAS for counter-drone applications, Prism Ground ISR and Prism SKR. Together, these products provide perception and image-processing capabilities for systems including UAS, ground surveillance platforms and automatic target recognition applications.
Prism A-ISR combines AI perception models, edge processing and multispectral sensor support. The company says its patented denoise and super-resolution technology can improve signal quality while reducing bandwidth requirements, and electronic stabilization and dehaze algorithms aim to improve image quality and extend detection range. Jared Faraudo, Vice President of Product Management at Teledyne FLIR OEM, said the software improves long-range situational awareness in cluttered and degraded visual environments while reducing false alarms and integration risk.
The rollout hinges on how well the module integrates with the embedded AI platforms and camera families the company names. For developers and defense integrators, the payoff will depend on whether the promised faster model adaptation through AIMMGen translates into shorter deployment cycles for new mission targets.
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