Automatic Regularization for Linear MMSE Filters

Daniel Gomes de Pinho Zanco, Leszek Szczecinski, Jacob Benesty
12/11/2023
cs.ITcs.LGeess.SP

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Abstract

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.

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Cite this paper

@article{zanco2023automatic,
  title  = {Automatic Regularization for Linear MMSE Filters},
  author = {Daniel Gomes de Pinho Zanco and Leszek Szczecinski and Jacob Benesty},
  year   = {2023},
  eprint = {2312.06560},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.06560v2}
}

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