Signal Modeling
Signal Processing Toolbox™ provides parametric modeling techniques that let you estimate a rational transfer function that describes a signal, system, or process. Use known information about a signal to find the coefficients of a linear system that models it. Approximate a given time-domain impulse response using Prony and Steiglitz-McBride ARX models. Find an analog or digital transfer function that matches a given complex frequency response. Model resonances using linear prediction filters.
Fonctions
Rubriques
- Linear Prediction and Autoregressive Modeling
Compare two methods for determining the parameters of a linear filter: autoregressive modeling and linear prediction.
- 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.