1 <?xml version="1.0" encoding="UTF-8"?>
2 <refentry xmlns="http://docbook.org/ns/docbook" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:svg="http://www.w3.org/2000/svg" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:db="http://docbook.org/ns/docbook" xmlns:scilab="http://www.scilab.org" xml:lang="en" xml:id="sskf">
4 <refname>sskf</refname>
5 <refpurpose>steady-state Kalman filter</refpurpose>
8 <title>Calling Sequence</title>
9 <synopsis>[xe,pe]=sskf(y,f,h,q,r,x0)</synopsis>
12 <title>Arguments</title>
18 data in form <literal>[y0,y1,...,yn]</literal>, <literal>yk</literal> a column vector
25 <para>system matrix dim(NxN)</para>
31 <para>observations matrix dim(MxN)</para>
37 <para>dynamics noise matrix dim(NxN)</para>
43 <para>observations noise matrix dim(MxM)</para>
49 <para>initial state estimate</para>
55 <para>estimated state</para>
61 <para>steady-state error covariance</para>
67 <title>Description</title>
69 steady-state Kalman filter
73 <title>Examples</title>
74 <programlisting role="example"><![CDATA[
77 q=[.03 0.01;.01 0.03];
83 x0=m0+(chol(p0))'*rand(2,1);
86 v=(chol(r))'*rand(2,11);
89 [xe]=sskf(y,f,h,q,r,m0)