  We design a sequential Monte Carlo scheme for the dual purpose of Bayesian
inference and model selection. We consider the application context of urban
mobility, where several modalities of transport and different measurement
devices can be employed. Therefore, we address the joint problem of online
tracking and detection of the current modality. For this purpose, we use
interacting parallel particle filters, each one addressing a different model.
They cooperate for providing a global estimator of the variable of interest
and, at the same time, an approximation of the posterior density of each model
given the data. The interaction occurs by a parsimonious distribution of the
computational effort, with online adaptation for the number of particles of
each filter according to the posterior probability of the corresponding model.
The resulting scheme is simple and flexible. We have tested the novel technique
in different numerical experiments with artificial and real data, which confirm
the robustness of the proposed scheme.
