The goal of filtering is the estimation of a true state of a process with erroneous measurements.

For a efficient implementation it is desirable to use recursive algorithms, with a defined process model for the  relation between different time steps. Potential model errors can be tackled with uncertainties.

A prominent recursive filter is the Kalman-Filter which is an optimal estimator for linear, additive and Gaussian process and measurement noise.