2017
DOI: 10.1016/j.dsp.2016.09.011
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Cooperative parallel particle filters for online model selection and applications to urban mobility

Abstract: 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 … Show more

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Cited by 136 publications
(115 citation statements)
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“…Particle filters (PFs) are implementations of recursive Bayesian filters which approximate the posterior PDF by a set of random samples, called particles, with associated weights. Several types of PFs have been developed over the last few years [1][2][3][4][5][6][7][8]. They differ in their choice of the importance sampling density and the resampling step.…”
Section: Introductionmentioning
confidence: 99%
“…Particle filters (PFs) are implementations of recursive Bayesian filters which approximate the posterior PDF by a set of random samples, called particles, with associated weights. Several types of PFs have been developed over the last few years [1][2][3][4][5][6][7][8]. They differ in their choice of the importance sampling density and the resampling step.…”
Section: Introductionmentioning
confidence: 99%
“…On the contrary, an SMC-based alternative for filtering time-series of different properties could be to pose the problem from a model selection perspective [36][37][38]. However, we study a different and more ambitious approach in this paper, as we target inference of latent states with different characteristics in a unified and consistent manner.…”
Section: Contributionmentioning
confidence: 99%
“…Related to the problem under study, it is worth saying that several contributions deal with the filtering problem in nonlinear/non-Gaussian SSM under model uncertainty using sequential Monte Carlo (SMC) methods, for instance, joint state and parameter estimation solutions [20], model selection strategies using interacting parallel PFs [21,22], or model information fusion within the SMC formulation [23]. The main drawback of SMC methods is their high computational complexity and the curse-ofdimensionality [24].…”
Section: State-of-the-artmentioning
confidence: 99%