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Справка Scilab >> Signal Processing > filters > lindquist

lindquist

Lindquist's algorithm

Calling Sequence

[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

  • srfaur — square-root algorithm
  • faurre — filter computation by simple Faurre algorithm
  • phc — Markovian representation
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Last updated:
Thu Oct 02 14:01:06 CEST 2014