Kalman gain is refined through 2D correlation mining to improve wind disturbance compensation and antenna positioning under variable conditions.
Neural networks augment Kalman filtering to correct sensor bias and unknown controls while preserving interpretable state estimation for autonomous vehicles.
Confidence-interval checks and a secondary Kalman filter catch velocity overestimation before braking force reduction affects vehicle stability.
Real-time analysis of synchronized IED data pinpoints power generation events and their propagation to speed grid recovery.
Phase shifters and resonator pairs keep magnetic-resonance power transfer stable despite position changes, reducing control complexity.