The proliferation of small unmanned aerial systems (sUAS), commercially known as drones, is an asymmetric security and safety issue. Enforce Field employs computer vision and machine learning integrated with full motion video sensors to detect, identify, and track UAS aircraft for unprecedented situational awareness for military and civilian aviation personnel.
- Tactical - Lightweight FMV passive sensor allows C-UAS operations in non-radar (EMCON) environments
- Low Bandwidth - 100-200 kbps per video source (30 fps)
- Diverse Operations - Installation on stationary and mobile platforms (buildings, towers, vehicles, drones, aircraft)
- Disparate Sources - Compatible with camera systems employing full range of optical characteristics, pixel density, and spectrum (visual, EO/IR, full)
- Target Agnostic - Detect, classify, and track unmanned drone and piloted aircraft allowing complete situational awareness of operational airspace.
- Near real-time PNT (Position Navigation and Timing) data (course, speed, altitude, range, bearing)
- Predictive and empirical machine learning for track classification and detection confidence level
- IFF capability for user-defined drone/aircraft classification and response
- JSON data feed for third party systems (ATAK, GIS, AR/VR)
- Night operations capability with appropriate optics hardware
- Timestamp and positioning allow local or remote server operation