.png){kind=icon}
返回
Distributed Consensus-Based Multitarget Filtering and Its Application in Formation-Containment Control
.png){kind=icon}
PRE
AI
DOI：10.1109/TCNS.2019.2926281.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper studies a distributed multitarget filtering problem for a sensor network where each sensor can obtain the measurements and system information of some targets while having no knowledge of others. To estimate the states of all targets, a consensus Kalman information filtering algorithm with an adaptive and finite-time matrix-weighted consensus strategy is proposed. When the communication network is strongly connected and the sensing network is time-varying while being always collectively observable, it is proved that if the targets' system matrices are time invariant, the mean-square estimation errors of the sensors are bounded for any number of consensus iterations. If the targets' system matrices are time varying and the number of the consensus steps per information filtering is larger than the diameter of the communication topology, the mean-square estimation errors of the sensors are also bounded. When each sensor is intermittently activated to observe the targets and the network does not remain collectively observable, an allowable lower bound of detection probability is derived to guarantee the stochastic boundedness of the estimation errors. Then, the filtering algorithm is applied to design a distributed containment controller for multiple agents to encircle multiple planar heterogeneous dynamic targets. Finally, simulation examples are given to illustrate the effectiveness of the algorithms.
Keyword:
Containment control
distributed filtering
matrix-weighted consensus
multiagent systems
multitarget filtering
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
论文数:
1.7K
被引数:
5.8K
机构
引用论文
分享收藏
Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏分享收藏A fully distributed weight design approach to consensus Kalman filtering for sensor networksAUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏分享收藏
查看更多内容