srkf
====

square root Kalman filter



Calling Sequence
~~~~~~~~~~~~~~~~


::

    [x1,p1]=srkf(y,x0,p0,f,h,q,r)




Arguments
~~~~~~~~~

:f, h current system matrices
: :q, r covariance matrices of dynamics and observation noise
: :x0, p0 state estimate and error variance at t=0 based on data up to
  t=-1
: :y current observation Output from the function is
: :x1, p1 updated estimate and error covariance at t=1 based on data
  up to t=0
:



Description
~~~~~~~~~~~

square root Kalman filter algorithm