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Wiener filtering 6.011, Spring 2018 Lec 20 1 Unconstrained Wiener filter structure - m x m y y [ n ] x [ n ] + + h [] 2 Unconstrained Wiener filter solution - m x m y D yx ( e j ) H ( e j ) = x [ n ] y [ n ] + + D xx ( e j ) 3

Wiener filtering 6.011, Spring 2018 Lec 20 1
Unconstrained Wiener filter structure - m x m y y [ n ] x [ n ] + + h [·] 2
Unconstrained Wiener filter solution - m x m y D yx ( e j Æ ) H ( e j Æ ) = x [ n ] y [ n ] + + D xx ( e j Æ ) 3
Compared with static LMMSE estimator - m x m y D yx ( e j Æ ) H ( e j Æ ) = x [ n ] y [ n ] + + D xx ( e j Æ ) - m X m Y T c X Y ( C XX ) - 1 + + X Y 4
MIT OpenCourseWare https://ocw.mit.edu 6.011 Signals, Systems and Inference Spring 201 8 For information about citing these materials or our Terms of Use, visit: https://ocw.mit.edu/terms. 5

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