  An incremental/online state dynamic learning method is proposed for
identification of the nonlinear Gaussian state space models. The method embeds
the stochastic variational sparse Gaussian process as the probabilistic state
dynamic model inside a particle filter framework. Model updating is done at
measurement sample rate using stochastic gradient descent based optimization
implemented in the state estimation filtering loop. The performance of the
proposed method is compared with state-of-the-art Gaussian process based batch
learning methods. Finally, it is shown that the state estimation performance
significantly improves due to the online learning of state dynamics.
