Power spectral density estimate using modified covariance method
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. 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 allpole 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 samplebased vector (row, column, or 1D) or framebased vector (column only). This input represents a frame of consecutive time samples from a singlechannel 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 zeropads 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 timedomain 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 YuleWalker Method blocks.
The dspsacomp
dspsacomp
example
compares the modified covariance method with several other spectral
estimation methods.
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.
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.
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 zeropads 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.
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 timedomain 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.
Specify the sample time of the original timedomain signal. This parameter becomes visible only when you clear the Inherit sample time from input check box.
Kay, S. M. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: PrenticeHall, 1988.
Marple, S. L. Jr., Digital Spectral Analysis with Applications. Englewood Cliffs, NJ: PrenticeHall, 1987.
Orfanidis, S. J. Introduction to Signal Processing. Englewood Cliffs, NJ: PrenticeHall, 1995.
Port  Supported Data Types 

Input 

Output 

The output data type is the same as the input data type.
Burg Method  DSP System Toolbox 
Covariance Method  DSP System Toolbox 
Modified Covariance AR Estimator  DSP System Toolbox 
ShortTime FFT  DSP System Toolbox 
YuleWalker Method  DSP System Toolbox 
See Spectral Analysis for related information.