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Automatic regularization for linear MMSE filters
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DOI：10.1016/j.sigpro.2024.109820.png)
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Abstract
En 中文
In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the Bayesian approach, the regularization parameter is found from the observed signals via simple closed-form fixed-point iteration. The proposed automatic regularization approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results.
Keywords:
MMSE filter
Regularization
Bayesian approach
System identification
Beamforming
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