A survey of nonlinear Bayesian filtering algorithms
Ying Tan · Electronics Optics & Control · 2008
The main goal of filtering is to obtain,recursively in time,optimal estimation and prediction of the dynamical systems and error statistics from the sequential observations.Various nonlinear filtering algorithms are reviewed and interpreted in a unified way using the recursive Bayesian estimation.According to different approximation methods,these approximate nonlinear filters can be categorized into three types:analytical approximations,deterministic-sampling based approaches,and stochastic-sampling based filters.Then the principles,methods and characteristics of above nonlinear filters are analyzed and reviewed in detail.Finally,some representative new developments of the nonlinear filtering are described,and further research prospects are introduced.