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The representation of the process, X t , is the sequence of random variables X i . The choice basis of i t is unrestricted. Of particular interest is to restrict the basis functions to those which make the X i uncorrelated random variables. When this requirement is satisfied, the resulting representationof X t is termed the Karhunen-Love expansion. Mathematically, we require i j i j X i X j X i X j . This requirement can be expressed in terms of the correlation function of X t . X i X j 0 T X i 0 T X j 0 T 0 T i j R X As X i is given by X i 0 T m X i our requirement becomes

i j i j 0 T 0 T i j R X 0 T m X i 0 T m X j
Simple manipulations result in the expression
i j i j 0 T i 0 T K X j 0
When i j , the quantity X i 2 X i 2 is just the variance of X i . Our requirement is obtained by satisfying 0 T i 0 T K X j i i j or
i j i j 0 T i g j 0
where g j 0 T K X j Furthermore, this requirement must hold for each j which differs from the choice of i . A choice of a function g j satisfying this requirement is a function which is proportional to j : g j j j . Therefore,
0 T K X j j j
The i which allow the representation of X t to be a sequence of uncorrelated random variables must satisfy this integral equation. This type of equation occursoften in applied mathematics; it is termed the eigenequation . The sequences i and i are the eigenfunctions and eigenvalues of K X , the covariance function of X t . It is easily verified that: K X t u i 1 i i t i u This result is termed Mercer's Theorem .

The approach to solving for the eigenfunction and eigenvalues of K X t u is to convert the integral equation into an ordinary differential equation which can be solved. This approach isbest illustrated by an example.

K X t u 2 t u . The eigenequation can be written in this case as 2 u 0 t u u t u t T u t Evaluating the first derivative of this expression, 2 t t 2 u t T u 2 t t t t or 2 u t T u t Evaluating the derivative of the last expression yields the simple equation 2 t 2 t This equation has a general solution of the form t A t B t . It is easily seen that B must be zero. The amplitude A is found by requiring 1 . To find , one must return to the original integral equation. Substituting, wehave 2 A u 0 t u u 2 t A u t T u A t After some manipulation, we find that t t 0 T A t A t T A t or t t 0 T A t T 0 Therefore, n n 1 2 T n 1/2 and we have n 2 T 2 n 1/2 2 2 n t 2 T 1/2 n 1/2 t T

The Karhunen-Love expansion has several important properties.

  • The eigenfunctions of a positive-definite covariance function constitute a complete set. One can easily show thatthese eigenfunctions are also mutually orthogonal with respect to both the usual inner product and with respect to the innerproduct derived from the covariance function.
  • If X t Gaussian, X i are Gaussian random variables. As the random variables X i are uncorrelated and Gaussian, the X i comprise a sequence of statistically independent random variables.
  • Assume K X t u N 0 2 t u : the stochastic process X t is white. Then u N 0 2 t u u t for all t . Consequently, if i N 0 2 , this constraint equation is satisfied no matter what choice is made for the orthonormal set i t . Therefore, the representation of white, Gaussian processes consists of a sequence of statisticallyindependent, identically-distributed (mean zero and variance N 0 2 ) Gaussian random variables. This example constitutes the simplest case of the Karhunen-Love expansion.

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Source:  OpenStax, Statistical signal processing. OpenStax CNX. Dec 05, 2011 Download for free at http://cnx.org/content/col11382/1.1
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