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In a realization of the state space form 
   
the impulse response 
   
is preserved by the action 
   
that is, 
   
where T is a nonsingular   matrix. This corresponds to the
change of coordinates of the state vector 
   
When we choose the filter parameters   we can exploit
this symmetry to obtain choices which, although having the same signal
properties, have superior numerical or computational properties. Since these
realizations have the same impulse response, they are called observationally equivalent.
 
After imposing some more or less unobjectionable constraints on the filter,
it turns out that any two observationally equivalent systems are related by
this type of coordinate change. Note: we should go into minimality,
observability and reachability in somewhat more detail here.
 
A system of dimension d is called minimal if it is not possible to
realize the impulse response with a system of lower dimension. This means
that each component of   actually is used at some point, and that the
distribution of   vectors actually fills up d-dimensions. However,
it can happen that a minimal realization of an impulse response involves
coordinates for   in which the vectors   are distributed in such
a way that the variation in some directions is very small compared to
others, i.e. that the ellipsoids of constant probability are very far from
spheres. It should not be too surprising that this can expose a finite
precision realization of the system to numerical pathologies. It is
therefore desirable that all the components of   have similar
scaling.
 
It is impossible to obtain this property without any information about  . However, if it is known that   is relatively noisy, then it
is possible to get good results by choosing the coordinates for   in
such a way that   would be spherically symmetrically distributed if
the   were independent and identically distributed. It turns out that
this means choosing   so that Stein's equation is
satisfied 
   
and determining c from the system 
   
where 
   
Note that 
   
but that thanks to the Stein equation, we have 
   
which converges as long as A is stable. (We are only interested in stable A, so we can always use this). Since 
   
it follows that   is column unitary, and we can solve for the least
squares approximation to c by 
   
This corresponds to expanding the impulse response in the orthogonal
functions given by the columns of  , and the `Fourier' coefficients
of the impulse response are the components of c.
 
   
 
We have not exhausted the possible choices of coordinates for  , since
by Shur's decomposition, we can always arrange for A to be triangular with
a unitary choice of T. The unitarity of T preserves the Stein equation 
   
and we use this choice to reduce A to a triangular matrix. A system where   satisfy the Stein equation with A triangular is called
a Triangular Input Balanced (TIB) system. This choice is particularly
good since in addition to the good numerical properties implied by the Stein
equation, it is possible to compute   in  
operations, as opposed to the usual   for general A.
 
    
          
 Next: Meixner Functions and TIB 
Up: Recursive Windowing of Time 
 Previous: Comparison With a Rectangular 
  
Fri Jun 27 03:10:38 EDT 1997
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