Problem Being Solved:
Autonomous UAV navigation in GPS-denied or GPS-compromised environments, where jamming, spoofing, or obstruction removes the absolute positioning reference required for mission execution.

Why This Matters:
Without GPS, navigation becomes a state estimation problem under uncertainty, and small sensor errors accumulate into large position drift, causing loss of situational awareness and mission failure.

Core Approach:
Stella replaces satellite dependence with onboard sensing and physics-based modeling, treating navigation as a probabilistic estimation task rather than a pattern-recognition problem.

How It Works:
It fuses visual odometry, inertial measurements, barometric altitude, and magnetic heading using a nonlinear state estimator that predicts motion through physical dynamics and corrects it using sensor observations.

Design Philosophy:
The system prioritizes physics-driven models and probabilistic sensor fusion over pure machine learning to ensure interpretability, predictable behavior, and robustness in unseen environments.

Error and Drift Management:
Sensor noise and bias are explicitly modeled, and uncertainty is propagated and corrected at every step, enabling bounded error growth and graceful degradation when individual sensors become unreliable.

Outcome:
Stella enables resilient, real-time waypoint navigation in GNSS-denied conditions by maintaining continuous pose estimation using only onboard sensing and uncertainty-aware fusion.

TAM–SAM–SOM
The global autonomous and defense UAV navigation market represents a $15B total addressable market (TAM), with GPS-denied and resilient navigation systems comprising a $9B serviceable available market (SAM), and an initial serviceable obtainable market of $500M focused on defense, industrial inspection, and high-risk autonomous operations (SOM).