Main Content

Root MUSIC algorithm

estimates the frequency content in the input signal `w`

= rootmusic(`x`

,`p`

)`x`

and returns
`w`

, a vector of frequencies in rad/sample. 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`

] = rootmusic(___,`'corr'`

)`x`

to be interpreted as a correlation matrix
rather than a 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`

.

If the input signal `x`

is real, and an odd number of sinusoids is
specified by `p`

, an error message is displayed:

Real signals require an even number p of complex sinusoids.

The multiple signal classification (MUSIC) algorithm used by `rootmusic`

is the same as that used by `pmusic`

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

The difference between `pmusic`

and `rootmusic`

is:

`pmusic`

returns the pseudospectrum at all frequency samples.`rootmusic`

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

`rootmusic`

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