The world’s leading RF intelligent platform demonstrates low-signature, mission-ready sensing for SOF operators facing rapidly evolving UAS threats
Hidden Level showcased its advanced passive RF sensing and multi-platform counter-UAS integration capabilities at SOF Week 2026, highlighting low-signature technologies designed to help Special Operations Forces detect and track evolving drone threats in contested environments.
The demonstrations focused on improving tactical situational awareness, force protection, and operational decision-making without compromising mission concealment.
Passive RF Sensing For Tactical Operations
According to Hidden Level, modern uncrewed aerial system (UAS) threats are compressing decision timelines and increasing risks for small units and forward operating sites.
The company’s sensing platform combines:
- Passive radar
- RF detection
- Direction-finding technology
The system is capable of detecting both cooperative and non-cooperative drones while identifying operator locations without emitting signals that could reveal force positions.
Designed for both vehicle-mounted and fixed-site deployments, the technology provides persistent low-altitude airspace awareness even in low-bandwidth environments.
Open Architecture And Platform Integration
Hidden Level emphasized the interoperability of its open, non-proprietary architecture, allowing integration with existing SOF command-and-control systems and secure information sharing with allied and interagency partners.
At SOF Week, the company demonstrated live Airspace Monitoring Service (AMS) streaming through multiple command-and-control platforms, including:
- RAIN
- Lattice
- Sit(x)
- TAK
The demonstrations showcased how passive RF sensing can reduce cognitive load, accelerate threat understanding, and improve mission assurance at the tactical edge.
Supporting Special Operations Missions
Brad Garber, COO of Hidden Level, said:
“SOCOM operators are facing a rapidly evolving UAS threat environment where low-cost drones can disrupt missions and put personnel at risk in seconds.”
He added that early threat detection without compromising operational position is critical for Special Operations Forces operating in contested environments.
The company noted that its technology has already been field-tested in demanding operational settings, including deployments supporting the United States Army Special Operations Command (USASOC), where it served as a primary detection layer for Group I UAS and larger aerial and surface threats.
SOF Week Demonstration Locations
Hidden Level featured its technology at multiple locations during SOF Week 2026 in Tampa:
- Ultra I&C – Westin Waterside “Coral Reef Room”
- Global Ordnance / MHS Pavilion
- Booz Allen Hamilton JW Marriott Florida Ballroom
The demonstrations included live AMS streaming, Surge passive RF sensor displays, and integration showcases across partner platforms.

Hidden Level develops RF intelligence platforms that combine passive sensing, persistent data, and applied intelligence to support safety and security operations across air, ground, sea, and space environments.
Internal Links URLs
https://security.world/
External Links URLs
https://www.hiddenlevel.com/
Frequently Asked Questions (FAQs)
1. What Did Hidden Level Showcase At SOF Week 2026?
Hidden Level demonstrated passive RF sensing, counter-UAS integration, and multi-platform situational awareness technologies.
2. What Is Passive RF Sensing?
Passive RF sensing detects drones and RF activity without emitting signals that could reveal the operator’s position.
3. Which Platforms Integrated Hidden Level’s AMS Data?
AMS data was streamed through RAIN, Lattice, Sit(x), and TAK command-and-control platforms.
4. Who Uses Hidden Level’s Technology?
The company stated that its systems have been field-tested with the United States Army Special Operations Command (USASOC).
5. What Threats Can The System Detect?
The platform can detect cooperative and non-cooperative drones, including Group I UAS and larger aerial and surface targets.
Source: hiddenlevel.com