Consider first ergodicity in the mean. A time average inline for the mean of random process X(t) is given in (8.8). We know that if X(t) has a constant mean, then inline: the expected value of the time average, even for finite T, is the mean of the random process so that inline is an unbiased estimator of μ X. Ergodicity in the mean is a stronger property such that the time average itself converges to the mean as inline.

Definition: Ergodic in Mean (Process) Random process X(t) with constant mean μ X is ergodic in the mean if

(8.14) Numbered Display Equation

When the time average approaches its expectation as implied above, its variance approaches zero. This is a very useful property: the mean of a random process can be accurately estimated by averaging any realization over a sufficiently long time interval. Ergodicity in the mean is verified as follows.

Theorem 8.1 (Ergodic in Mean). Random process X(t) with autocovariance function CXX(τ) is ergodic in the mean if and only if




Proof. Consider the ...

Get Probability, Random Variables, and Random Processes: Theory and Signal Processing Applications now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.