.png){kind=icon}
返回
Adaptive Consensus-Based Distributed Target Tracking With Dynamic Cluster in Sensor Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2018.2805717.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 is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter's information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.
Keyword:
Adaptive filtering
data fusion
distributed Kalman filtering
dynamic cluster selection
sensor networks
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks基于机器学习和卡尔曼滤波的无线传感器网络目标跟踪.png)
IEEE SENSORS JOURNAL
OAAI
IEEE SENSORS JOURNALIF4.5
OAAI分享收藏Distributed Optimal Consensus Filter for Target Tracking in Heterogeneous Sensor Networks异构传感器网络目标跟踪的分布式最优一致性滤波器
PREAI
分享收藏Cluster-Based Consensus Time Synchronization for Wireless Sensor Networks基于簇的无线传感器网络一致性时间同步IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏分享收藏
查看更多内容