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.

翻译：本文研究了最小均方误差(MMSE)线性滤波器设计中的正则化问题。通过建立与统计机器学习方法的联系，采用贝叶斯方法，从观测信号中以简单自动的方式确定正则化参数。所提方法在系统辨识和波束成形示例中得到验证，结果表明自动正则化能够产生接近最优的结果。