Lecture Signals, systems & inference – Lecture 16: Wide-sense stationary processes; LTI filtering of WSS processes. The following will be discussed in this chapter: Random process; iid signal x[n], uniform in []; y=h*x, with h[n] = δ[n] + δ[n-1]; y=h*x, with h[n] = () n u[n]; |H| when h[n]=() n u[n]. | Lecture Signals, systems & inference – Lecture 16: Wide-sense stationary processes; LTI filtering of WSS processes Wide-sense stationary processes; LTI filtering of WSS processes , Spring 2018 Lec 16 1 Random process ° Amplitude X(t; c) c t1 t 2 Signal ensemble for outcomes a,b,c,d; & determination of RXX(t1,t2) X(t) = xXaa(t) (t) t xbb(t) X(t) = X (t) t xcc(t) X(t) = X (t) t xdd(t) X(t) = X (t) t t1 3 t2 Courtesy of Alex Albright. Used with permission. Weather plot was generated with code adapted from Bradley Boehmke. 4 iid signal x[n], uniform in [] 5 y=h*x, with h[n] = δ[n] + δ[n-1] 6 y=h*x, with h[n] = δ[n] - δ[n-1] 7 y=h*x, with h[n] = ()n u[n] 8 |H| when h[n]=()n u[n] 9 MIT OpenCourseWare Signals, Systems and Inference Spring 2018 For information about citing these materials or our Terms of Use, visit: . 10
