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Stochastic models and methods for multi-object tracking
The problem of multiple-object tracking consists in the recursive estimation ofthe state of several targets by using the information coming from an observation... -
Initialization of Particle Filter and Posterior Cramér-Rao Bound for Bearings...
We here address the classical bearings-only tracking problem (BOT) for a single object, which belongs to the general class of non linear filtering problems. Recently,... -
Performance Analysis of two Sequential Monte Carlo Methods and Posterior Cram...
The multi-target tracking algorithms generally present two basic ingredients: - an estimation algorithm coupled with a data association method. In the last years, the... -
Bayesian inference for the reconstruction of complex flows. Application on NA...
This thesis takes place in the framework of the calibration of low order models from experimental sequences acquired by time resolved PIV around at profil NACA0012... -
Non-asymptotic deviation inequalities for smoothed additive functionals in no...
International audience -
Convergence of a Particle-based Approximation of the Block Online Expectation...
International audience -
Monte Carlo Methods for Channel, Phase Noise and Frequency Offset Estimation ...
International audience -
Sequential Monte Carlo smoothing for general state space hidden Markov models
International audience -
Overview of Bayesian sequential Monte Carlo methods for group and extended ob...
This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured... -
Monte Carlo EM methods and Particle approximations: Application to the calibr...
This thesis pursues a double perspective in the joint use of sequential Monte Carlo methods (SMC) and the Expectation-Maximization algorithm (EM) under hidden Markov...
