Tag: estimation

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 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.

The moving average filter reduces noise while preserving the dynamic behavior of time-varying signals by averaging only a recent window of measurements.