Estimate AR model parameters using covariance method
a = arcov(x,p)
[a,e] = arcov(x,p)
a = arcov(x,p) uses the covariance method to fit a pth order autoregressive (AR) model to the input signal, x, which is assumed to be the output of an AR system driven by white noise. This method minimizes the forward prediction error in the least-squares sense. The vector a contains the normalized estimate of the AR system parameters, A(z), in descending powers of z. Let y(n) be a wide-sense stationary random process obtained by filtering a white noise input with variance e with the system function A(z). If is the power spectral density of y(n), then:
Because the method characterizes the input data using an all-pole model, the correct choice of the model order p is important.