DSP Blockset

Modified Covariance AR Estimator

Compute an estimate of AR model parameters using the modified covariance method

Library

Estimation / Parametric Estimation

Description

The Modified Covariance AR Estimator block uses the modified covariance method to fit an autoregressive (AR) model to the input data. This method minimizes the forward and backward prediction errors in the least squares sense. The input is a frame of consecutive time samples, which is assumed to be the output of an AR system driven by white noise. The block computes the normalized estimate of the AR system parameters, A(z), independently for each successive input.

The order, p, of the all-pole model is specified by the Order parameter.

The output port labeled A outputs the normalized estimate of the AR model coefficients in descending powers of z.

• [1 a(2) ... a(p+1)]
  

The scalar gain, G, is output from the output port labeled G.

Dialog Box

Estimation order

The order of the AR model, p.

References

Kay, S. M. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1988.

Marple, S. L., Jr., Digital Spectral Analysis with Applications. Englewood Cliffs, NJ: Prentice-Hall, 1987.

Supported Data Types

• Double-precision floating point
• Single-precision floating point

To learn how to convert your data types to the above data types in MATLAB and Simulink, see Supported Data Types and How to Convert to Them.

See Also

Burg AR Estimator	DSP Blockset
Covariance AR Estimator	DSP Blockset
Modified Covariance Method	DSP Blockset
Yule-Walker AR Estimator	DSP Blockset
armcov	Signal Processing Toolbox

Minimum

Modified Covariance Method