Unscented Kalman filters and Particle Filter methods for nonlinear state estimation

被引：49

作者：

Gyoergy, Katalin ^{[1
]}

Kelemen, Andras ^{[1
]}

David, Laszlo ^{[1
]}

机构：

[1] Sapientia Univ, Fac Tech & Human Sci, Dept Elect Engn, Targu Mures 540485, Romania

来源：

7TH INTERNATIONAL CONFERENCE INTERDISCIPLINARITY IN ENGINEERING (INTER-ENG 2013) | 2014年 / 12卷

关键词：

nonlinear state estimation; Unscented Kalman Filter; Particle Filter algorithm;

D O I：

10.1016/j.protcy.2013.12.457

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

For nonlinear state space models to resolve the state estimation problem is difficult or these problems usually do not admit analytic solution. The Extended Kalman Filter (EKF) algorithm is the widely used method for solving nonlinear state estimation applications. This method applies the standard linear Kalman filter algorithm with linearization of the nonlinear system. This algorithm requires that the process and observation noises are Gaussian distributed. The Unscented Kalman Filter (UKF) is a derivative-free alternative method, and it is using one statistical linearization technique. The Particle Filter (PF) methods are recursive implementations of Monte-Carlo based statistical signal processing. The PF algorithm does not require either of the noises to be Gaussian and the posterior probabilities are represented by a set of randomly chosen weighted (C) 2013 The Authors. Published by Elsevier Ltd.

引用

页码：65 / 74

页数：10