信号建模

线性预测、自回归 (AR) 模型、Yule-Walker、Levinson-Durbin

Signal Processing Toolbox™ 提供参数化建模方法，可让您估计描述信号、系统或过程的有理传递函数。使用信号的已知信息来查找对其建模的线性系统的系数。使用 Prony 和 Steiglitz-McBride ARX 模型逼近给定的时域脉冲响应。找到与给定复频率响应匹配的模拟或数字传递函数。使用线性预测滤波器对共振建模。

函数

全部展开

线性预测编码

`corrmtx`	Data matrix for autocorrelation matrix estimation
`levinson`	Levinson-Durbin recursion
`lpc`	Linear prediction filter coefficients
`rlevinson`	Reverse Levinson-Durbin recursion
`schurrc`	Compute reflection coefficients from autocorrelation sequence
`xcorr`	互相关
`xcov`	互协方差
`ac2poly`	Convert autocorrelation sequence to prediction polynomial
`ac2rc`	Convert autocorrelation sequence to reflection coefficients
`is2rc`	Convert inverse sine parameters to reflection coefficients
`lar2rc`	Convert log area ratio parameters to reflection coefficients
`lsf2poly`	Convert line spectral frequencies to prediction filter coefficients
`poly2ac`	Convert prediction filter polynomial to autocorrelation sequence
`poly2lsf`	Convert prediction filter coefficients to line spectral frequencies
`poly2rc`	Convert prediction filter polynomial to reflection coefficients
`rc2ac`	Convert reflection coefficients to autocorrelation sequence
`rc2is`	Convert reflection coefficients to inverse sine parameters
`rc2lar`	Convert reflection coefficients to log area ratio parameters
`rc2poly`	Convert reflection coefficients to prediction filter polynomial

参数化建模

`arburg`	Autoregressive all-pole model parameters — Burg’s method
`arcov`	Autoregressive all-pole model parameters — covariance method
`armcov`	Autoregressive all-pole model parameters — modified covariance method
`aryule`	Autoregressive all-pole model parameters — Yule-Walker method
`invfreqs`	Identify continuous-time filter parameters from frequency response data
`invfreqz`	Identify discrete-time filter parameters from frequency response data
`prony`	Prony method for filter design
`stmcb`	Compute linear model using Steiglitz-McBride iteration

主题

线性预测和自回归建模
比较确定线性滤波器参数的两种方法：自回归建模和线性预测。
AR Order Selection with Partial Autocorrelation Sequence
Assess the order of an autoregressive model using the partial autocorrelation sequence.
Parametric Modeling
Study techniques that find the parameters for a mathematical model describing a signal, system, or process.
Prediction Polynomial
Obtain the prediction polynomial from an autocorrelation sequence. Verify that the resulting prediction polynomial has an inverse that produces a stable all-pole filter.