Scilab 6.0.2
- Справка Scilab
- Signal Processing
- Filters
- How to design an elliptic filter
- analpf
- buttmag
- casc
- cheb1mag
- cheb2mag
- ell1mag
- eqfir
- eqiir
- faurre
- ffilt
- filt_sinc
- filter
- find_freq
- frmag
- fsfirlin
- group
- hilbert
- iir
- iirgroup
- iirlp
- kalm
- lev
- levin
- lindquist
- remez
- remezb
- srfaur
- srkf
- sskf
- syredi
- system
- trans
- wfir
- wfir_gui
- wiener
- wigner
- window
- yulewalk
- zpbutt
- zpch1
- zpch2
- zpell
Please note that the recommended version of Scilab is 2025.0.0. This page might be outdated.
See the recommended documentation of this function
lindquist
Lindquist's algorithm
Syntax
[P,R,T]=lindquist(n,H,F,G,R0)
Arguments
- n
number of iterations.
- H, F, G
estimated triple from the covariance sequence of
y
.- R0
E(yk*yk')
- P
solution of the Riccati equation after n iterations.
- R, T
gain matrices of the filter.
Description
computes iteratively the minimal solution of the algebraic
Riccati equation and gives the matrices R
and T
of the
filter model, by the Lindquist's algorithm.
Example
//Generate signal x=%pi/10:%pi/10:102.4*%pi; y=[1; 1] * sin(x) + [sin(2*x); sin(1.9*x)] + rand(2,1024,"normal"); //Compute correlations c=[]; for j=1:2 for k=1:2 c=[c;corr(y(k,:),y(j,:),64)]; end end c=matrix(c,2,128); //Find H,F,G with 6 states hk=hank(20,20,c); [H,F,G]=phc(hk,2,6); //Solve Riccati equation R0=c(1:2,1:2); [P,R,T]=lindquist(100,H,F,G,R0);
See also
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