Modified Covariance AR Estimator (DSP Blockset)

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

Library

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 top output, A, contains the normalized estimate of the AR model coefficients in descending powers of z,

```
[1 a(2) ... a(p+1)]
```

The scalar gain, G, is provided at the bottom output (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 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

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

Also see Parametric Estimation for a list of all the blocks in the Parametric Estimation library.

Minimum Modified Covariance Method