| Signal Processing Blockset™ | ![]() |
Estimation / Power Spectrum Estimation
dspspect3
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. To guarantee a valid output, you must set the Estimation order parameter to be less than or equal to two thirds the input vector length. 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 Nfft equally spaced frequency points in the range [0,Fs), where Fs is the signal's sample frequency.
When you select Inherit FFT length from estimation order, Nfft is one greater than the estimation order. When you do not select Inherit FFT length from estimation order, Nfft is specified as a power of 2 by the FFT length parameter, and the block zero pads or wraps the input to Nfft 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.
The dspsacomp demo compares the modified covariance method with several other spectral estimation methods.

The order of the AR model.
When selected, the FFT length is one greater than the estimation order.
Enter the number of data points on which to perform the FFT, Nfft. When Nfft is larger than the input frame size, each frame is zero-padded as needed. When Nfft is smaller than the input frame size, each frame is wrapped as needed. This parameter is enabled when you clear the Inherit FFT length from estimation order check box.
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.
| Port | Supported Data Types |
|---|---|
Input |
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Output |
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The output data type is the same as the input data type.
| Burg Method | Signal Processing Blockset |
| Covariance Method | Signal Processing Blockset |
| Modified Covariance AR Estimator | Signal Processing Blockset |
| Short-Time FFT | Signal Processing Blockset |
| Yule-Walker Method | Signal Processing Blockset |
| pmcov | Signal Processing Toolbox |
See Power Spectrum Estimation for related information.
![]() | Modified Covariance AR Estimator | Multiphase Clock | ![]() |
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