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Covariance Method - Power spectral density estimate using covariance method

Library

Estimation / Power Spectrum Estimation

dspspect3

Description

The Covariance Method block estimates the power spectral density (PSD) of the input using the covariance method. This method fits an autoregressive (AR) model to the signal by minimizing the forward prediction error in the least squares sense. The Estimation order parameter specifies the order of the all-pole model. The block computes the spectrum from the FFT of the estimated AR model parameters. To guarantee a valid output, the Estimation order parameter must be less than or equal to half the input vector length.

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

Selecting Inherit FFT length from estimation order, specifies that N_fft is one greater than the estimation order. Clearing the Inherit FFT length from estimation order check box allows you to use the FFT length parameter to specify N_fft as a power of 2. The block zero-pads or wraps the input to N_fft before computing the FFT. The output is always sample based.

When you select the Inherit sample time from input check box, the block computes the frequency data from the sample period of the input signal. For the block to produce valid output, the following conditions must hold:

• The input to the block is the original signal, with no samples added or deleted (by insertion of zeros, for example).

• The sample period of the time-domain signal in the simulation equals the sample period of the original time series.

If these conditions do not hold, clear the Inherit sample time from input check box. You can then specify a sample time using the Sample time of original time series parameter.

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

Dialog Box

Estimation order

The order of the AR model. To guarantee a nonsingular output, the value of this parameter must be less than or equal to half the input length.

Inherit FFT length from estimation order

When selected, this option specifies that the FFT length is one greater than the estimation order.

FFT length

Enter the number of data points on which to perform the FFT, N_fft. When N_fft is larger than the input frame size, the block zero-pads each frame as needed. When N_fft is smaller than the input frame size, the block wraps each frame as needed. This parameter becomes visible only when you clear the Inherit FFT length from estimation order check box.

Inherit sample time from input

When you select the Inherit sample time from input check box, the block computes the frequency data from the sample period of the input signal. For the block to produce valid output, the following conditions must hold:

• The input to the block is the original signal, with no samples added or deleted (by insertion of zeros, for example).

• The sample period of the time-domain signal in the simulation equals the sample period of the original time series.

If these conditions do not hold, clear the Inherit sample time from input check box. You can then specify a sample time using the Sample time of original time series parameter.

Sample time of original time series

Specify the sample time of the original time-domain signal. This parameter becomes visible only when you clear the Inherit sample time from input check box.

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.

Orfanidis, S. J. Introduction to Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1995.

Supported Data Types

Port	Supported Data Types
Input	Double-precision floating point Single-precision floating point
Output	Double-precision floating point Single-precision floating point

See Also

Burg Method	Signal Processing Blockset
Covariance AR Estimator	Signal Processing Blockset
Modified Covariance Method	Signal Processing Blockset
Short-Time FFT	Signal Processing Blockset
Yule-Walker Method	Signal Processing Blockset
spectrum.cov	Signal Processing Toolbox

See Power Spectrum Estimation for related information.

Covariance AR Estimator

Create Diagonal Matrix

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