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 3.0. From then on you should only use the new module model_estimation instead.
LSIIR(Hvals, Nb, Na, f, Fs, tau=0, justFit=False)¶
Least-squares IIR filter fit to a given frequency response.
This method uses Gauss-Newton non-linear optimization and pole mapping for filter stabilization
- 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) –
- b,a (IIR filter coefficients as numpy arrays)
- tau (filter time delay in samples)
LSFIR(H, N, tau, f, Fs, Wt=None)¶
Least-squares fit of a digital FIR filter to a given frequency response.
- 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,)