Covariance AR Estimator
Compute estimate of autoregressive (AR) model parameters using covariance method
Estimation / Parametric Estimation
The input must be a column vector or an unoriented vector, which is assumed to be the output of an AR system driven by white noise. This input represents a frame of consecutive time samples from a single-channel signal. The block computes the normalized estimate of the AR system parameters, A(z), independently for each successive input frame.
The order, p, of the all-pole model is 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 half the input vector length.
The top output,
A, is a column vector of length
p+1 with the same frame status as the input, and contains the
normalized estimate of the AR model coefficients in descending powers of
[1 a(2) ... a(p+1)]
The scalar gain, G, is provided at the bottom output
See the Burg AR Estimator block reference page for a comparison of the Burg AR Estimator, Covariance AR Estimator, Modified Covariance AR Estimator, and Yule-Walker AR Estimator blocks.
- Estimation order
The order of the AR model, p. To guarantee a nonsingular output, you must set p to be less than or equal to half the input length. Otherwise, the output might be singular.
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
|Port||Supported Data Types|
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Generated code relies on the
memset function (
string.h) under certain