  In this contribution, we present an online method for joint state and
parameter estimation in jump Markov non-linear systems (JMNLS). State inference
is enabled via the use of particle filters which makes the method applicable to
a wide range of non-linear models. To exploit the inherent structure of JMNLS,
we design a Rao-Blackwellized particle filter (RBPF) where the discrete mode is
marginalized out analytically. This results in an efficient implementation of
the algorithm and reduces the estimation error variance. The proposed RBPF is
then used to compute, recursively in time, smoothed estimates of complete data
sufficient statistics. Together with the online expectation maximization
algorithm, this enables recursive identification of unknown model parameters.
The performance of the method is illustrated in simulations and on a
localization problem in wireless networks using real data.
