Fitting filters to frequency response

This module contains several functions to carry out a least-squares fit to a given complex frequency response.

This module contains the following functions:

  • LSIIR: Least-squares IIR filter fit to a given frequency response
  • LSFIR: Least-squares fit of a digital FIR filter to a given frequency response

Deprecated since version 1.2.71: The module identification will be combined with the module deconvolution and renamed to model_estimation in the next major release 2.0.0. From then on you should only use the new module model_estimation instead.

PyDynamic.identification.fit_filter.LSIIR(Hvals, Nb, Na, f, Fs, tau=0, justFit=False)[source]

Least-squares IIR filter fit to a given frequency response.

This method uses Gauss-Newton non-linear optimization and pole mapping for filter stabilization

Parameters:
  • Hvals (numpy array of frequency response values of shape (M,)) –
  • Nb (integer numerator polynomial order) –
  • Na (integer denominator polynomial order) –
  • f (numpy array of frequencies at which Hvals is given of shape) –
  • (M,)
  • Fs (sampling frequency) –
  • tau (integer initial estimate of time delay) –
  • justFit (boolean, when true then no stabilization is carried out) –
Returns:

  • b,a (IIR filter coefficients as numpy arrays)
  • tau (filter time delay in samples)

References

PyDynamic.identification.fit_filter.LSFIR(H, N, tau, f, Fs, Wt=None)[source]

Least-squares fit of a digital FIR filter to a given frequency response.

Parameters:
  • H (frequency response values of shape (M,)) –
  • N (FIR filter order) –
  • tau (delay of filter) –
  • f (frequencies of shape (M,)) –
  • Fs (sampling frequency of digital filter) –
  • Wt ((optional) vector of weights of shape (M,) or shape (M,M)) –
Returns:

Return type:

filter coefficients bFIR (ndarray) of shape (N+1,)