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We design a sequential Monte Carlo scheme for the dual purpose of Bayesianinference and model selection. We consider the application context of urbanmobility, where several modalities of transport and different measurementdevices can be employed. Therefore, we address the joint problem of onlinetracking and detection of the current modality. For this purpose, we useinteracting parallel particle filters, each one addressing a different model.They cooperate for providing a global estimator of the variable of interestand, at the same time, an approximation of the posterior density of each modelgiven the data. The interaction occurs by a parsimonious distribution of thecomputational effort, with online adaptation for the number of particles ofeach filter according to the posterior probability of the corresponding model.The resulting scheme is simple and flexible. We have tested the novel techniquein different numerical experiments with artificial and real data, which confirmthe robustness of the proposed scheme.

Cooperative Parallel Particle Filters for online model selection and applications to Urban Mobility