.png){kind=icon}
返回
State estimation over packet dropping networks using multiple description coding
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2006.03.020.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For state estimation over a communication network, efficiency and reliability of the network are critical issues. The presence of packet dropping and communication delay can greatly impair our ability to measure and predict the state of a dynamic process. In this paper, multiple description (MD) codes, a type of network source codes, are used to compensate for this effect on Kalman filtering. We consider two packet dropping models: in one model, packet dropping occurs according to an independent and identically distributed (i.i.d.) Bernoulli random process and in the other model, packet dropping is bursty and occurs according to a Markov chain. We show that MD codes greatly improve the statistical stability and performance of Kalman filter over a large set of packet loss scenarios in both cases. Our conclusions are verified by simulation results. (C) 2006 Elsevier Ltd. All rights reserved.
Keyword:
state estimation
packet dropping
Kalman filter
multiple description coding
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
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容