期刊
PROCEEDINGS OF THE IEEE
卷 95, 期 5, 页码 899-924出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2007.893250
关键词
bayesian dynamical model; filtering; prediction and smoothing; hidden Markov models; parameter estimation; particle filter; sequential Monte Carlo; state-space model; tracking
It is now over a decade since the pioneering contribution of Gordon et al. (1993), which is commonly regarded as the first instance of modern sequential Monte Carlo (SMC) approaches. initially focussed on applications to tracking and vision, these techniques are now very widespread and have had a significant impact in virtually all areas of signal and image processing concerned with Bayesian dynamical models. This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.
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