6. Adaptive filters
Please refer to
6.1 Adaptability
Determining the Wiener filter for a noisy stationary signal, as described in the previous paragraph, requires knowledge of the second-order moments of the "signal, noise" pair. In reality, this information is often unavailable: an adaptive method can be envisaged, based, for example, on iterative optimization such as stochastic gradient or recursive least squares. Note that such adaptive methods are frequently used in non-stationary signal processing. Here again, we restrict ourselves to the case of real, discrete, monovariable, scalar quantities, bearing in mind that these structures generalize to multivariable, vector cases.
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Adaptive filters
Pattern recognition in noisy signal processing
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