Autoregressive power spectral density estimate — Yule-Walker method

returns
the power spectral density estimate, `pxx`

= pyulear(`x`

,`order`

)`pxx`

, of
a discrete-time signal, `x`

, found using the Yule-Walker
method. When `x`

is a vector, it is treated as
a single channel. When `x`

is a matrix, the PSD
is computed independently for each column and stored in the corresponding
column of `pxx`

. `pxx`

is the
distribution of power per unit frequency. The frequency is expressed
in units of rad/sample. `order`

is the order of
the autoregressive (AR) model used to produce the PSD estimate.

`[`

returns
a frequency vector, `pxx`

,`f`

] = pyulear(___,`fs`

)`f`

, in cycles per unit time.
The sampling frequency, `fs`

, is the number of
samples per unit time. If the unit of time is seconds, then `f`

is
in cycles/second (Hz). For real–valued signals, `f`

spans
the interval [0,`fs`

/2] when `nfft`

is
even and [0,`fs`

/2) when `nfft`

is
odd. For complex-valued signals, `f`

spans the
interval [0,`fs`

).

`[`

returns
the two-sided AR PSD estimates at the frequencies specified in the
vector, `pxx`

,`f`

] = pyulear(`x`

,`order`

,`f`

,`fs`

)`f`

. The vector, `f`

,
must contain at least two elements. The frequencies in `f`

are
in cycles per unit time. The sampling frequency, `fs`

,
is the number of samples per unit time. If the unit of time is seconds,
then `f`

is in cycles/second (Hz).

`[___,`

returns
the `pxxc`

] = pyulear(___,'ConfidenceLevel',`probability`

)`probability`

× 100%
confidence intervals for the PSD estimate in `pxxc`

.

`pyulear(___)`

with no output
arguments plots the AR PSD estimate in dB per unit frequency in the
current figure window.

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