Consensus-Based Distributed Multiple Model UKF for Jump Markov Nonlinear Systems

Wenling Li

doi:10.1109/TAC.2011.2161838

2012-01-01

168

PRE

李文玲 (Wenling Li) *

Yingmin Jia

DOI：10.1109/TAC.2011.2161838

原文链接

原文求助

摘要

En 中文

This note studies the problem of distributed estimation for jump Markov nonlinear systems (JMNLSs) in a not fully connected sensor network. Based on the consensus theory, a distributed unscented Kalman filter (UKF) is first derived for nonlinear systems without jumping parameters and then it is extended to develop a distributed multiple model UKF for JMNLSs. The proposed filtering algorithm is illustrated via a simulation example involving tracking a maneuvering target.

Keyword:

Consensus theory

distributed estimation

jump Markov nonlinear system (JMNLS)

unscented Kalman filter (UKF)

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

IEEE Transactions on Automatic Control

IF:

论文数:

1.3W

被引数:

6.7W

机构

Beihang University

学者数:

5.2W

论文数: 4.1W

被引数: 37

引用论文

5G-MiEdge: Design, standardization and deployment of 5G phase II technologies: MEC and mmWaves joint development for Tokyo 2020 Olympic games

2017 IEEE Conference on Standards for Communications and Networking (CSCN)

IF0

2017-09-01

PREAI

Valerio Frascolla; Federico Miatton; Gia Khanh Tran; Koji Takinami; Antonio De Domenico; Emilio Calvanese Strinati; Konstantin Koslowski; Thomas Haustein; Kei Sakaguchi; Sergio Barbarossa; Sergio Barberis

Nonlinear Estimation and Multiple Sensor Fusion Using Unscented Information Filtering

ieee signal processing magazine

IF3.9

2008-01-01

230

PREAI

Lee, Deok-Jin

Distributed Kriged Kalman Filter for Spatial Estimation

IEEE TRANSACTIONS ON AUTOMATIC CONTROL

IF7

2009-12-01

201