  Simultaneous state and parameter estimation arises from various applicational
areas but presents a major computational challenge. Most available Markov chain
or sequential Monte Carlo techniques are applicable to relatively low
dimensional problems only. Alternative methods, such as the ensemble Kalman
filter or other ensemble transform filters have, on the other hand, been
successfully applied to high dimensional state estimation problems. In this
paper, we propose an extension of these techniques to high dimensional state
space models which depend on a few unknown parameters. More specifically, we
combine the ensemble Kalman-Bucy filter for the continuous-time filtering
problem with a generalized ensemble transform particle filter for intermittent
parameter updates. We demonstrate the performance of this two stage update
filter for a wave equation with unknown wave velocity parameter.
