Sequential parameter estimation of time-varying non-Gaussian autoregressive processes
EURASIP Journal on Applied Signal Processing
Learning Generative Models for Multi-Activity Body Pose Estimation
International Journal of Computer Vision
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
International Journal of Computer Vision
An offline bidirectional tracking scheme
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Blind estimation of fast time-varying multi-antenna channels based on sequential monte carlo method
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
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We develop methods for performing filtering and smoothing in nonlinear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.