Particle filtering and moving horizon estimation
Particle filtering and moving horizon estimation
复制标题
DOI：
10.1016/j.compchemeng.2006.05.031
复制
发表时间：
2006-09-12
影响因子：
4.3
通讯作者：
Bakshi, Bhavik R.
中科院分区：
文献类型：
--
作者：
Rawlings, James B.;Bakshi, Bhavik R.
本文综述了目前可用的线性，约束和非线性系统的状态估计方法。讨论了卡尔曼滤波、扩展卡尔曼滤波、无迹卡尔曼滤波、粒子滤波和滚动时域估计等方法。对粒子滤波和滚动时域估计的研究现状进行了综述，分析了这两种方法的优缺点。新的研究主题，建议解决结合最佳功能的滚动时域估计和粒子滤波器。(c)2006爱思唯尔有限公司保留所有权利。
This paper provides an overview of currently available methods for state estimation of linear, constrained and nonlinear systems. The following methods are discussed: Kalman filtering, extended Kalman filtering, unscented Kalman filtering, particle filtering, and moving horizon estimation. The current research literature on particle filtering and moving horizon estimation is reviewed, and the advantages, and disadvantages of these methods are presented. Topics for new research are suggested that address combining the best features of moving horizon estimation and particle filters. (c) 2006 Elsevier Ltd. All rights reserved.