Neural Bias Estimation Layer
A lightweight learning module monitors IMU behavior and predicts dynamic bias corrections. Instead of assuming constant bias, the system adaptively learns drift patterns during flight.
Phase-4
Learning-Aided INS Drift Suppression Layer
Extending stabilization into full GPS-denied navigation reliability.
Phase 4 extends the stabilization framework into a long-duration navigation support system. While Phase 3 ensured robust disturbance rejection, this phase introduces an intelligence layer that actively suppresses cumulative inertial drift over time.
01 - What Is Being Added
New Intelligence Layer Components
Sensor Fusion Integration
Predicted corrections are injected into the EKF pipeline, improving state estimation accuracy and reducing cumulative position and orientation error growth.
Long-Duration Validation
The system will be tested across extended flight time, multiple wind regimes, altitude variations, and checkpoint transitions to measure sustained drift suppression.
02 - Evaluation Focus
Evaluation Focus
-Drift reduction percentage
-Error growth rate comparison
-Stability retention over time
-Classical INS+EKF vs Learning-Aided EKF benchmark
03 - What Phase 4 Achieves
Navigation Reliability Layer
- Transitions from stabilization to navigation reliability
- Enables longer autonomous missions without GPS
- Builds a deployable GPS-denied autonomy stack
- Makes the system mission-ready
Upcoming Development Phase
Phase 4 introduces a learning-based bias correction layer integrated with sensor fusion to suppress cumulative INS drift, enabling stable and reliable long-duration GPS-denied autonomous flight.