Square-root Cubature Kalman Filter (SCKS)
This toolbox contains implementation of square-root Cubature
Kalman Filter and square-root Rauch-Tang-Striebel smoother (SCKF-SCKS).
These algorithms perform joint estimation of the states, input
and parameters of stochastic continuous-discrete state-space
models. The state equations must have a form of ordinary differential
equations, where their discretization is performed through an
efficient local-linearization scheme. Additionally, the parameter
noise covariance is estimated dynamicaly via stochastic Robbins-Monro
approximation method, and the measurement noise covariance is
estimated online as well, using combination of varitional Bayesian
(VB) approach with nonlinear filter/smoother. In particular,
this method was designed to perform the nonlinear blind deconvolution
of hemodynamic responses from fMRI data to estimate the underlying
neuronal signal. Please contact Martin Havlicek (email@example.com)
for any questions or suggestions.
Detailed description of this method can be found in our paper
that has been accepted to NeuroImage:
This software is distributed under the
GNU General Public License (version 2 or later); please
refer to the file License.txt
, included with the software, for details.