Defense & Security

Validate autonomy where real-world testing isn't an option.

Reconstruct the mission corridor. Simulate the sensor. Prove the perception stack against the scenarios a real mission will face — before you burn an airframe, a vessel, or a physical testing ground.

Software augmented testing for defense & security perception.

Real-fidelity neural reconstructions and deterministic sensor simulation to build, test, and validate autonomous security and surveillance perception across every condition and threat scenario.

Challenges with Defense & Security Perception Development

Safe, Reliable Perception

Edge Cases — Rare security events (intrusions, unauthorized access, suspicious behavior) are inherently dangerous and nearly impossible to stage realistically.

Observability — Model changes can behave differently across camera angles, lighting conditions, and facility layouts.

Safety — Testing threat detection with real actors in restricted areas carries significant safety and liability risk.

Solution — Continuously evaluate perception across threat scenarios, lighting, weather, and facility configurations in a virtual environment.

Time and Cost

Staging realistic threat scenarios requires significant resources: actors, restricted access areas, coordinated timing across multiple cameras. Each iteration is costly and time-consuming to reproduce.

Scalability Across Sites and Sensor Networks

Security systems must perform reliably across different facilities, environments, lighting conditions, and camera configurations. Achieving consistent detection accuracy at scale through physical testing alone is impractical.

PD Solutions

PD Replica + PD Sim for defense perception and mission rehearsal.

Evaluate

Open-loop and closed-loop testing integration for security perception stacks. Nightly regression testing for intrusion detection, perimeter breach recognition, and threat identification. Perception unit testing across lighting, weather, and camera perspectives. Near-validation testing in real-world scanned facilities (PD Replica).

Analyze

Quantify false-positive rates, missed detections, and response-time accuracy across thousands of synthetic threat scenarios and camera perspectives.

Train

Generate diverse, labeled training data for person detection, behavior analysis, and anomaly recognition — including rare events that are dangerous or impossible to stage.

Benefits

Addressing Industry Challenges

Test threats you can't safely stage

Simulate intrusions, unauthorized access, and suspicious behavior in synthetic environments — without real-world risk or complex logistics.

Scale across sites and camera networks

Generate diverse scenarios across facility types, camera angles, lighting conditions, and sensor configurations to ensure consistent detection performance.

Reduce false alarms, increase trust

Use synthetic data to train models that distinguish real threats from benign activity, improving detection accuracy and reducing costly false positives.