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Autoregressive power spectral density estimate — covariance method

returns
the power spectral density (PSD) estimate, `pxx`

= pcov(`x`

,`order`

)`pxx`

,
of a discrete-time signal, `x`

, found using the
covariance 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
the vector of normalized angular frequencies, `pxx`

,`w`

] = pcov(___)`w`

,
at which the PSD is estimated. `w`

has units of
radians/sample. For real-valued signals, `w`

spans
the interval [0, *π*] when `nfft`

is
even and [0,*π*) when `nfft`

is
odd. For complex–valued signals, `w`

always
spans the interval [0,2*π*].

`[`

returns
a frequency vector, `pxx`

,`f`

] = pcov(___,`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`

] = pcov(`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`

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

)`probability`

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

.

`pcov(___)`

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

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