Minimum mean-squared error (MMSE) filtering is a powerful and widely used technique that uses the available data to design an optimum set of filter weights. Choosing weights based on the data allows the weights to be adjusted or adapted to maintain an optimal solution in the presence of time-varying characteristics in the input data. You will learn that many different applications of MMSE filtering are expressed in a standard form where the objective is to find filter weights that minimize the error between a reference signal and the filter output. Specifically, interference suppression, system modeling or identification, and equalization are three of the many applications that are illustrated in this lesson. Using a standard form enables you to use a common framework and toolset to solve many different problems.
MMSE filtering is a rich and useful set of skills to add to your repertoire.