Lecture Signals, systems & inference – Lecture 20: Wiener filtering. The following will be discussed in this chapter: Unconstrained Wiener filter structure, unconstrained Wiener filter solution, compared with static LMMSE estimator. | Lecture Signals, systems & inference – Lecture 20: Wiener filtering Wiener filtering , Spring 2018 Lec 20 1 Unconstrained Wiener filter structure -mx my x[n] + h[·] + y[n] 2 Unconstrained Wiener filter solution -mx my Dyx(e jÆ) x[n] + H(e jÆ) = + y[n] Dxx(e jÆ) 3 Compared with static LMMSE estimator -mx my Dyx(e jÆ) x[n] + H(e jÆ) = + y[n] Dxx(e jÆ) -mX mY X + cTXY (CXX)-1 + Y 4 MIT OpenCourseWare Signals, Systems and Inference Spring 2018 For information about citing these materials or our Terms of Use, visit: . 5
