Main Content

Frequency and power content using eigenvector method

`[`

estimates the frequency content in the input signal `w`

,`pow`

] = rooteig(`x`

,`p`

)`x`

and returns
`w`

, a vector of frequencies in rad/sample, and the corresponding
signal power in the vector `pow`

. You can specify the signal subspace
dimension using the input argument `p`

.

The extra threshold parameter in the second entry in `p`

provides you
more flexibility and control in assigning the noise and signal subspaces.

`[`

forces the input argument `w`

,`pow`

] = rooteig(___,`'corr'`

)`x`

to be interpreted as a correlation matrix
rather than matrix of signal data. For this syntax, `x`

must be a square
matrix, and all of its eigenvalues must be nonnegative. This syntax can include the input
arguments from the previous syntax.

**Note**

You can place `'corr'`

anywhere after `p`

.

The eigenvector method used by `rooteig`

is the same as that used by
`peig`

. The algorithm performs eigenspace analysis of the signal's correlation
matrix to estimate the signal's frequency content.

The difference between `peig`

and `rooteig`

is:

`peig`

returns the pseudospectrum at all frequency samples.`rooteig`

returns the estimated discrete frequency spectrum, along with the corresponding signal power estimates.

`rooteig`

is most useful for frequency estimation of signals made up of a
sum of sinusoids embedded in additive white Gaussian noise.