Modified Covariance Method

Power spectral density estimate using modified covariance method

Library

Estimation / Power Spectrum Estimation

dspspect3

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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. It does so by minimizing the forward and backward prediction errors in the least squares sense. The Estimation order parameter value specifies the order of the all-pole model. To guarantee a valid output, the Estimation order parameter must be less than or equal to two thirds of the input vector length. The block computes the spectrum from the FFT of the estimated AR model parameters.

The input must be a column vector. This input 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 parameter 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.

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.

Parameters

Estimation order

Specify the order of the AR model. To guarantee a valid output, the Estimation order parameter must be less than or equal to two thirds of the input vector length.

Inherit FFT length from estimation order

When you select this check box, the option specifies that the FFT length is one greater than the estimation order. To specify the number of points on which to perform the FFT, clear this check box. You can then specify a power of two FFT length using the FFT length parameter.

FFT length

Enter the number of data points, N_fft, on which to perform the 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

If 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

The output data type is the same as the input data type.