Automatic Regularization for Linear MMSE Filters
This provides an incremental improvement for researchers and practitioners in signal processing by automating regularization in filter design.
The paper tackles the problem of automatically determining the regularization parameter for linear MMSE filters, using a Bayesian approach to derive it from observed signals, and demonstrates near-optimal results in system identification and beamforming examples.
In this work, we consider the problem of regularization in the design of minimum mean square error (MMSE) linear filters. Using the relationship with statistical machine learning methods, using a Bayesian approach, the regularization parameter is found from the observed signals in a simple and automatic manner. The proposed approach is illustrated in system identification and beamforming examples, where the automatic regularization is shown to yield near-optimal results.