Modified Covariance Method (DSP Blockset)

Compute a parametric spectral estimate using the modified covariance method.

Library

Description

The Modified Covariance Method block estimates the power spectral density (PSD) of the input using the modified covariance method. This method fits an autoregressive (AR) model to the signal by minimizing the forward and backward prediction errors in the least-squares sense. The order of the all-pole model is the value specified by the Estimation order parameter, and the spectrum is computed from the FFT of the estimated AR model parameters.

The input is a sample-based vector (row, column, or 1-D) or frame-based vector (column only) representing a frame of consecutive time samples from a single-channel signal. The block's output (a column vector) is the estimate of the signal's power spectral density at N_fft equally spaced frequency points in the range [0,F_s), where F_s is the signal's sample frequency.

When Inherit FFT length from input dimensions is selected, N_fft is specified by the frame size of the input, which must be a power of 2. When Inherit FFT length from input dimensions is not selected, N_fft is specified as a power of 2 by the FFT length parameter, and the block zero pads or truncates the input to N_fft before computing the FFT. The output is always sample-based.

See the Burg Method block reference for a comparison of the Burg Method, Covariance Method, Modified Covariance Method, and Yule-Walker Method blocks.

Examples

The dspsacomp demo compares the modified covariance method with several other spectral estimation methods.

Dialog Box

Estimation order: The order of the AR model.
Inherit FFT length from input dimensions: When selected, uses the input frame size as the number of data points, N_fft, on which to perform the FFT. Tunable.
FFT length: The number of data points, N_fft, on which to perform the FFT. If N_fft exceeds the input frame size, the frame is zero-padded as needed. This parameter is enabled when Inherit FFT length from input dimensions is not selected.

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 Method
DSP Blockset

Covariance Method
DSP Blockset

Modified Covariance AR Estimator
DSP Blockset

Short-Time FFT
DSP Blockset

Yule-Walker Method
DSP Blockset

pmcov
Signal Processing Toolbox

Burg Method	DSP Blockset
Covariance Method	DSP Blockset
Modified Covariance AR Estimator	DSP Blockset
Short-Time FFT	DSP Blockset
Yule-Walker Method	DSP Blockset
`pmcov`	Signal Processing Toolbox

See Power Spectrum Estimation for related information. Also see a list of all blocks in the Power Spectrum Estimation library.

Modified Covariance AR Estimator Multiphase Clock