Neural networks can improve classical state estimation by learning adaptive forgetting factors that respond to real-time sensor quality, enabling robust UAV navigation during sensor outages and dynamic environments.
This paper presents a learned Kalman filter that adapts to changing noise conditions in UAVs by using a neural network to dynamically adjust how much it trusts past measurements. Instead of using a fixed forgetting factor, the filter learns a memory policy from sensor data, helping it handle sensor failures and vibrations better than traditional adaptive filters.