Tag: kalman-filter

The average filter is the simplest form of recursive estimation. Despite its simplicity, it provides strong noise reduction and forms the foundation of more advanced filters such as the Kalman filter.

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.

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.