This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: ECE 5510: Random Processes Lecture Notes Fall 2008 Lecture 19 Today: (1) Discussion of AA 5, (2) Several Example Random Processes • Assign HW 9, due Tuesday Nov 25 at 5pm in HW locker. • Application Assignment 5 due at 5pm today. (a) 5 10 15 0.5 1 1.5 2 2.5 Time Index n X n (b) 5 10 15 0.5 1 1.5 2 2.5 Time Index n Y n Figure 1: Realization of the (a) Bernoulli and (b) filtered Bernoulli process example covered in previous lecture. 1 Random Telegraph Wave Figure 2: The telegraph wave process is generated by switching between +1 and 1 at every arrival of a Poisson process. This was originally used to model the signal sent over telegraph lines. Today it is still useful in digital communications, and digital ECE 5510 Fall 2008 2 control systems. We model each flip as an arrival in a Poisson process. It is a model for a binary timevarying signal: X ( t ) = X (0)( − 1) N ( t ) Where X (0) is 1 with prob. 1/2, and 1 with prob. 1/2, and N ( t ) is a Poisson counting process with rate λ , (the number of arrivals in a Poisson process at time t ). X (0) is independent of N ( t ) for any time t . (Draw a graph.) 1. What is E X ( t ) [ X ( t )]? μ X ( t ) = E X ( t ) bracketleftBig X (0)( − 1) N ( t ) bracketrightBig = E X [ X (0)] E N bracketleftBig ( − 1) N ( t ) bracketrightBig = 0 · E N bracketleftBig ( − 1) N ( t ) bracketrightBig = 0 (1) 2. What is R X ( t,δ )? R X ( t,τ ) = E X bracketleftBig X (0)( − 1) N ( t ) X (0)( − 1) N ( t + τ ) bracketrightBig = E X bracketleftBig ( X (0)) 2 ( − 1) N ( t )+ N ( t + τ ) bracketrightBig = E N bracketleftBig ( − 1) N ( t )+ N ( t + τ ) bracketrightBig = E N bracketleftBig ( − 1) N ( t )+ N ( t )+( N ( t + τ ) N ( t )) bracketrightBig = E N bracketleftBig ( − 1) 2 N ( t ) ( − 1) ( N ( t + τ ) N ( t )) bracketrightBig = E N bracketleftBig ( − 1) ( N ( t + τ ) N ( t )) bracketrightBig Assume...
View
Full Document
 Fall '08
 Chen,R
 Brownian Motion, Autocorrelation, Stationary process, white Gaussian process

Click to edit the document details