square root Kalman filter
[x1,p1]=srkf(y,x0,p0,f,h,q,r)
current system matrices
covariance matrices of dynamics and observation noise
state estimate and error variance at t=0 based on data up to t=-1
current observation Output
updated estimate and error covariance at t=1 based on data up to t=0
This function is the square root form of Kalman filter. It is more efficient than the simple Kalman filter in term of numerical stability,
especially if dynamic noise covariance q
is small. In effect, that can provock an indefinite numerical representation
of the state covariance matrix p
, whereas p
is positive definite. So, the goal of srkf
is to propagate p
using a Cholesky factorization algorithm. These factors can be updated at each step of the algorithm, which
allows to keep p
in its basic form.