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Unformatted text preview: ECE 6010 Lecture 7 Analytical Properties of Random Processes Let X t be a function of time, and let h ( t ) be the impulse response of a (continuous time) linear time invariant system. If X t is the input to the system, then the output is Y t = X t * h ( t ) = Z  h ( t ) X d. We will consider such operations when X t is a random process. This will require us to develop some additional analytic properties of random processes. Continuity Let T = ( a, b ) . Recall that a function f : T R is continuous at t if lim t t f ( t ) = f ( t ) . f is continuous if it is continuous for every t ( a, b ) . Definition 1 For a random process { X t , t T } , we say X t is continuous with probability 1 if P ( { : X t ( ) , t T is continuous } ) = 1 . 2 Continuity w.p. 1 is usually quite strong, in fact stronger than is typically needed for analysis. We say that { X t , t T } is continuous in probability at t if X t X t (i.p.) as t t . That is, lim t t P (  X t X t  > ) = 0 for all > . We say that { X t , t T } is meansquare continuous at t if X t X t (m.s.) as t t . That is, lim t t E [  X t X t  2 ] = 0 . An important property: A secondorder random process is meansquare continuous at t = t if and only if R X ( t, s ) is continuous at t = s = t , that is lim t t ,s t R X ( t, s ) = R X ( t , t ) . Proof (If) Suppose R X ( t, s ) is continuous at t = s = t . Then E [  X t X t  2 ] = E [ X 2 t ] 2 E [ X t X t ]+ E [ X 2 t ] = ( R X ( t, t ) R X ( t, t ))+( R X ( t , t ) R X ( t, t )) Continuity of R X implies that lim t t R X ( t, t ) = lim t t R X ( t, t ) = R X ( t , t ) . So lim t t E [  X t X t  2 ] = 0 ....
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This note was uploaded on 03/01/2012 for the course ECE 6010 taught by Professor Stites,m during the Spring '08 term at Utah State University.
 Spring '08
 Stites,M

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