Non-Linear Noise Adaptive Kalman Filtering Via Variational Bayes
Simo Särkkä, Jouni Hartikainen

We consider joint estimation of state and time-varying noise covariance matrices in non-linear stochastic state space models. We propose a variational Bayes and Gaussian non-linear filtering based algorithm for efficient computation of the approximate filtering posterior distributions. The formulation allows the use 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.