Multiwindow estimators of correlation

Many algorithms for signal and array processing have embedded within them sample estimates of correlation. In this paper, we prove that the most general symmetric, quadratic, nonnegative-definite, modulation-invariant estimator of correlation is a multiwindow estimator. We establish that multiwindow...

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Bibliographic Details
Main Authors: McWhorter, L.T., Scharf, L.L.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive filters
Array signal processing
Covariance matrix
Delay estimation
Error correction
Fourier transforms
Signal processing
Signal processing algorithms
Spectral analysis
Symmetric matrices
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Summary:Many algorithms for signal and array processing have embedded within them sample estimates of correlation. In this paper, we prove that the most general symmetric, quadratic, nonnegative-definite, modulation-invariant estimator of correlation is a multiwindow estimator. We establish that multiwindow estimators have the potential to reduce estimator mean-squared error by reducing variance at the expense of controllable bias. When multiwindow estimators are used to solve signal and array processing problems, they have the potential to improve and generalize many standard results.< >
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.1994.471408