Modern conflicts are increasingly characterized by the use of small, RF-silent drones that can evade traditional radar and RF jamming systems.
These drones are often constructed from non-metallic materials and operated using fiber wires, rendering them largely immune to conventional detection methods and countermeasures.
As a result, defense and security forces face a critical gap in their ability to effectively detect, track, and neutralize these aerial threats, especially in air-to-air interception scenarios.
To address the critical challenge posed by small, RF-silent drones that evade conventional detection and interception systems, a comprehensive end-to-end solution has been developed.
This system leverages AI-driven processing, EO/IR sensors, and autonomous navigation to detect, track, identify, and intercept hostile UAVs in real time.
It incorporates RF-silent detection capabilities using EO and IR sensors video feed processed by edge-optimized neural networks, enabling it to identify threats that are invisible to traditional radar or RF jammers.
Autonomous interception functions dynamically guide interceptor UAVs for immediate engagement.
Designed to be hardware-agnostic, the system integrates seamlessly with a wide range of UAV platforms.
Its scalable and modular architecture ensures adaptability to various mission requirements, while its low Size, Weight, and Power (SWaP) profile and affordability allow for deployment on commercially available hardware without sacrificing performance or success rates.
