.png){kind=icon}
返回
Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements
.png){kind=icon}
OA
AI
DOI：10.3390/s24165299.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, the problem of estimating a scalar field (e.g., the spatial distribution of contaminants in an area) using a sensor network is considered. The sensors are assumed to have quantized measurements. We consider distributed estimation algorithms where each sensor forms its own estimate of the field, with sensors able to share information locally with its neighbours. Two schemes are proposed, called, respectively, measurement diffusion and estimate diffusion. In the measurement diffusion scheme, each sensor broadcasts to its neighbours the latest received measurements of every sensor in the network, while in the estimate diffusion scheme, each sensor will broadcast local estimates and Hessians to its neighbours. Information received from its neighbours will then be iteratively combined at each sensor to form the field estimates. Time-varying scalar fields can also be estimated using both the measurement diffusion and estimate diffusion schemes. Numerical studies illustrate the performance of the proposed algorithms, in particular demonstrating steady state performance close to that of centralized estimation.
Keyword:
distributed estimation
field estimation
quantized measurements
sensor networks
time-varying systems
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:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Studying Successful Public Plazas in the City of Murcia (Spain) Using a Ranking Nodes Algorithm and Foursquare Data使用排名节点算法和Foursquare数据研究西班牙穆尔西亚市成功的公共广场
.png){kind=icon}
PREAI
分享收藏
The relationship of MRI findings and clinical features in symptomatic and asymptomatic os naviculare
PREAI
分享收藏
查看更多内容