Time-based geolocation
Time-difference-of-arrival (TDOA) measurements feed the Location on a Conic Axis (LOCA) estimator.
FLAGSHIP TECHNOLOGY
Geometry-adaptive RF localization.
A Deep Q-Network research system for cooperative emitter geolocation and autonomous sensor positioning.
DESIGNED, ANALYZED & EVALUATED IN SIMULATION
THE IDEA
Estimate. Reposition. Measure again.
The system treats a cooperative group of UAV-based RF sensors as an array whose geometry can change. Localization and movement decisions form a repeated sensing loop.
SYSTEM COMPONENTS
Localization and autonomy working together.
Geometry control
A learned DQN policy selects sensor displacement actions using localization-related feedback and signal measurements.
Evaluation environment
Simulation supports controlled studies of channel conditions, sensor error, geometry, and repeated measurements.
RESEARCH EVIDENCE
A published simulation foundation.
The 2026 IEEE Aerospace paper reports Monte Carlo and EXata digital-twin evaluation of the geometry-adaptive DQN prototype. Its experiments examine localization refinement under modeled multipath, fading, and hardware inaccuracies.
Development status: research prototype.
Published evidence covers simulated scenarios. Hardware integration and flight evaluation are future development steps. Results depend on the scenario and measurement assumptions documented in the paper.
- Geometry Adaptive Deep Q-Network for UAV-Based Emitter Localization in Cluttered RF Environments 2026 IEEE Aerospace Conference · 2026
SUPPORTING TECHNOLOGIES
Explore the rest of the system.
CONNECT WITH KRONMERIC
Explore a research partnership.
Discuss cooperative RF sensing, algorithm evaluation, or a technology development opportunity.