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I have an input signal x (assumed to be iid Gaussian with $\mu=0$, $\sigma^2$) which is fed into two linear systems:

  • $y_1 = h_1 * x$
  • $y_2 = h_2 * x$

Now I would like to calculate $\mathbb{E}[y_1 y_2]$. What is the proper way to do this - preferably in the frequency domain?

Background: I want to calculate something like $\operatorname{var}(y_1 + y_2)$. This expands to $\operatorname{var}(y_1)+\operatorname{var}(y_2)+2\mathbb{E}[y_1 y_2]$. Now I am very familar with the first two components - the variance. I know that the variance of a stochastic signal is given by the autocorrelation sequence at position 0. With the Wiener-Kinchin theorem this translates to:

$$ \operatorname{var}(y_1) = r_{yy}(0) = \int_0^{\infty} \Phi_{yy}(f) df $$

and then:

$$ \cdots = \sigma_x^2 \int_0^{\infty} |H_1(f)|^2 df $$

Now for my problem - with $\mathbb{E}[y_1 y_2]$ I arrive at $\mathbb{E}[x^2 \cdots]$ (giving a variance) but the filter is not square magnitude.

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You can rewrite the convolution operator as a matrix operation by building a Toeplitz matrix for the impulse response function. Your equations can be rewritten as $Y_1 = H_1 X$ and $Y_2 = H_2 X$, where $Y_1 = [y^1_1,\ldots,y^n_1]^T$, $Y_2 = [y^1_2,\ldots,y^n_2]^T$, $X = [x_1,\ldots,x_m]^T$, $m$ is the length of the input vector, and $n$ is the length of the convolution product which depends on length of the input vector and the length of the impulse response function.

Now, $Y_1 \sim \mathcal{N}(0, \sigma^2 H_1 H_1^T)$, $Y_2 \sim \mathcal{N}(0, \sigma^2 H_2 H_2^T)$, and $X \sim \mathcal{N}(0, \sigma^2 I)$.

Therefore,

\begin{align} \mathbb{E}(Y_1 Y_2^T) &= \mathbb{E}(H_1 X X^T H_2^T) \\ &= H_1 \mathbb{E}(X X^T) H_2^T \\ &= \sigma^2 H_1 H_2^T \end{align}

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  • $\begingroup$ I was thinking about an approach such as that but couldn't figure out how to get $H$. Now I know. Thanks. $\endgroup$ – mark leeds Dec 19 '18 at 4:56

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