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Stein's Approach Based MVDR Filter Modification
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DOI：10.1109/LSP.2024.3379000.png)
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Abstract
En 中文
We consider a modification of the minimum variance distortionless response (MVDR) filter using Stein unbiased risk estimation (SURE). The starting point of this modification lies in the observation that the component of the MVDR filter in the subspace orthogonal to the signal of interest is the maximum likelihood estimate (MLE) of the location of a certain multivariate distribution. This draws us to consider James-Stein type estimates which have been shown to outperform MLE for minimization of some risks. In this letter we propose two kinds of modifications inspired by Stein's approach. A natural risk is defined and we derive a loss function which results in an unbiased estimate of this risk, then proceed to its minimization. Numerical simulations compare the so-modified MVDR filter to its original version.
Keywords:
Vectors
Maximum likelihood estimation
Covariance matrices
Signal to noise ratio
Training
Minimization
Loading
Adaptive filtering
Stein unbiased risk estimation
Journal
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