In
[1, p. 62 exercise 3.19] we are asked to find for the multiple sinusoidal process
the ensemble ACF and the temporal ACF as
, where the
‘s are all uniformly distributed random variables on
and independent of each other. We are also asked to determine if this random process is autocorrelation ergodic.
Solution:
We note that the joint p.d.f of the uniformly random variables
is given by
, with domain
we can proceed to calculate the ensemble autocorrelation function which is defined as:
As in exercise
[2] we can use the trigonometric relation
[3, p. 810] :
(
,
) to further simplify the expression for the ensemble autocorrelation function:
Having obtained the ensemble autocorrelation function we can proceed to obtain the temporal autocorrelation function, which we hope will be a good approximation
to the ensemble autocorrelation function
. By definition the temporal autocorrelation function is given by
The second sum of the previous relation can be simplified by one of the derived formulas of
[2], for
:
Thus the temporal autocorrelation function may be expressed, as long as
as:
In order to simplify the relation further, especially the first sum, we will again use (
1), with
and
, and thus :
We see that both distinct parts are of the form
, and thus it remains to simplify this relation. Again using a trigonometric formula
[3, p. 810]:
with
and
we can simplify the relation as:
Furthermore it was shown in
[2] that
, for
, so we can further simplify the previous relation by:
Thus finally we can simplify (
5) by:
So finally the temporal autocorrelation (
4) can be reduced using (
8) to the following formula:
Rearranging the terms on the right we see that the temporal autocorrelation function equals the ensemble autocorrelation function with an additional error
, which we have derived for the the case when
:
At once we see again that when
that the error goes to zero
. Thus the random process of the sum of sinusoids is also autocorrelation ergodic as long as
. It is also easy to recognize, by using the argumentation of the previous exercise
[2] , that this is not true if
does not hold. In this case parts of the the error of equation (
3) are proportional to
as was already observed in
[2] . QED.
[1] Steven M. Kay: “Modern Spectral Estimation – Theory and Applications”, Prentice Hall, ISBN: 0-13-598582-X. [2] Chatzichrisafis: “Solution of exercise 3.18 from Kay’s Modern Spectral Estimation -
Theory and Applications”. [3] Granino A. Korn and Theresa M. Korn: “Mathematical Handbook for Scientists and Engineers”, Dover, ISBN: 978-0-486-41147-7.
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