Tag: control-theory

The Extended Kalman Filter applies the Kalman filter to nonlinear systems by locally linearizing system and measurement models using Jacobians.

The Kalman filter is an optimal recursive estimator for linear systems, combining system models and noisy measurements through simple matrix operations.

This post explains the physical meaning of the error covariance P and the Kalman gain K, showing how the Kalman filter adaptively balances model prediction and sensor measurements.

A low pass filter reduces noise while emphasizing recent measurements, overcoming the limitations of uniform averaging in moving average filters.

Observability and controllability describe whether a system’s internal states can be inferred from outputs or driven by inputs. These concepts form the foundation of estimation and control in robotics and autonomous driving.