Train Smarter
Adapt faster with mission-specific synthetic data.
Why Teams Choose
TargetModeler
Train
Cut model training time with automated scenario creation.
Adapt
Execute
No-code visual tools eliminate hours of manual annotation.
What is
TargetModeler?
TargetModeler powers the ADAPT phase of SensorOps’ AI-native ecosystem.
It’s a no-code environment for rapidly generating synthetic data—built to reflect real-world physics, sensors, and conditions.
Teams use it to simulate environments for ISR, autonomous navigation, and industrial computer vision—turning edge-case gaps into data-rich strengths.
Advanced Scenario Builder
- Design layered missions using drag-and-drop logic
- Real-time adjustments during simulation
- Full playback with multi-perspective review
Multi-Environment Training
- Simulate operations in urban, woodland, desert, maritime, and subterranean terrain
- Dynamically control time-of-day, EO/IR degradation, weather, and threat behavior
Full Operational Integration
- Integrates directly with TAK (ATAK/WinTAK), GIS terrain data, and RF signal simulation
- Supports mission sync and digital twin generation
AI-Driven Agents
- Built-in pedestrian, vehicle, and drone logic
- Train for ISR, surveillance, strike, recon, and more — with EO/IR sensor modeling
SynMTRX
Test Autonomy Before Deployment
Use SynDOJO to test AI and autonomous agents using SynMTRX — a built-in
simulation evaluation mode:
Hardware-in-the-loop + software-in-the-loop support
Performance logging for AI/ML behavior in stress conditions
Simulate contested control environments without risk
Featured
Training Videos
Mission Strike +TAK Overlay
Woodland Car Swap Trail
Mission Strike +TAK Overlay
Want to see your environment modeled in
SensorOps?
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