  This paper is considered with joint estimation of state and time-varying
noise covariance matrices in non-linear stochastic state space models. We
present a variational Bayes and Gaussian filtering based algorithm for
efficient computation of the approximate filtering posterior distributions. The
Gaussian filtering based formulation of the non-linear state space model
computation allows usage of efficient Gaussian integration methods such as
unscented transform, cubature integration and Gauss-Hermite integration along
with the classical Taylor series approximations. The performance of the
algorithm is illustrated in a simulated application.
