Counter-UAS

Training and testing proven to close the sim2real gap — in every scenario

Drone threats are evolving fast. Drone detection AI needs to evolve faster. New drone designs, complex flight behaviors, novel environments — and defense systems need to keep pace. Our results are proven to close the sim2real gap, while dramatically reducing both costs and timelines.

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Find the data gaps, and fill them fast. Every failure caught in simulation prevents a surprise in the field, where the cost of being wrong is a missed threat, not a simple re-run.

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Full Sensor Library for Multispectral Detection
  • • Train and test your systems with the sensors it needs in the field: RGB, thermal, depth, and more, all rendered from the same physically accurate scene
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  • • Sensor digital twins are all customizable to match any real world counterpart

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Comprehensive Dashboard for Model Evaluation
  • • See precision, recall, and failure cases side by side against ground truth, so you know exactly where your model is falling short
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  • • Identify edge cases — at scale — long before the model is tested in the field

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Rapid Environment Variations
  • • Swap terrain, time of day, conditions in minutes instead of weeks, so your training data keeps pace with evolving operational realities
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  • • Rapidly introduce general and specific confusers, obstacles, and occlusions to hone model performance

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Precise Parametric Control for All Variables
  • • Dial in camera settings, weather, lighting, and drone behavior independently to generate exactly the edge cases your model is missing
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  • • Run precisely varied tests concurrently for a complete picture of model performance

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Book a demo
We'll reach out to discuss your project
See the results
See our approach in the real world

Learn More About Falcon for Counter-UAS

US Army’s Next Counter-Drone Weapon Starts in Simulation, Not a Lab
Integrity Threats in AI: When Data Poisoning Undermines Model Effectiveness