4. Optimum filtering
4.1 Problem position
The term optimal filtering means that filter design is not based on frequency or time specifications, but refers to a criterion such as mean square error.
Consequently, the aim of optimal linear filtering is to find the "best" linear filter, i.e. the one that provides an approximation such that the mean square error is minimal.
Common applications include noise reduction, prediction, inverse filtering, identification, detection coding and more.
One of the most important optimal filters in linear estimation is the Wiener filter.
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Optimum filtering
Kalman filtering
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