Covariance matrix of an adaptive filter input I run many times in equations containing the trace of covariance matrix of an adaptive filter input. But it is not really clear what it is.

For example in this paper the input covariance matrix is

$$\textbf{R} = E\left(\textbf{u}_i^*\textbf{u}_i \right)$$

where:


$\textbf{u}_i$ is row vector of input (seems to be regression vector in given time)
$^*$ denotes Hermitian conjugation (complex conjugation for scalars)
$E$ is not explained, but I would expect it is the expected value


Questions:


What does it mean? 
How does the matrix look like, what is the shape of it? 
How do I estimate it from input data $\textbf{U}$ - matrix where one row represents one input vector?
The Covariance Matrix is commonly defined as 

$$\mathbf Q = E\left[ (\mathbf x -\mathbf\mu_{x})(\mathbf x -\mathbf\mu_{x})^*\right]$$
with $\mu$ denoting the mean value, i.e. $\mu_{x}=E\left[\mathbf x\right]$, and $\mathbf x$ being column vectors. The fact that you define the covariance matrix as

$$\mathbf{R}_i = E\left[\textbf{u}_i^*\textbf{u}_i \right]$$

indicates that your $\mathbf u_i$ have zero mean and are row vectors; that might be a result of the signal model you use, but it's pretty non-standard.

Furthermore, the product of a column vector with its hermitian is inherently hermitian (symmetric if real).

Now, one way to estimate an expectation value from multiple observations. Wikipedia has an article on estimating covariance matrices, so here's just the gist: the most intuitive estimator is the sample covariance matrix; adapted to your formula, that'd be

$$\hat{\mathbf R} = \frac1{N-1}\sum\limits_{i=1}^N \mathbf u_i^*\mathbf u_i$$