Major Development Under Gaussian Filtering Since Unscented Kalman Filter  

作　　者：Abhinoy Kumar Singh 

机构地区：[1]IEEE [2]Department of Electrical Engineering,Indian Institute of Technology Indore

出　　处：《IEEE/CAA Journal of Automatica Sinica》2020年第5期1308-1325,共18页自动化学报（英文版）

摘　　要：Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.

关 键 词：Bayesian framework cubature rule-based filtering Gaussian filters Gaussian sum and square-root filtering nonlinear filtering quadrature rule-based filtering unscented transformation 

分 类 号：TN713[电子电信—电路与系统]