多个子网络间的时变图信号估计

Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka
{"title":"多个子网络间的时变图信号估计","authors":"Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka","doi":"arxiv-2409.10915","DOIUrl":null,"url":null,"abstract":"This paper presents an estimation method for time-varying graph signals among\nmultiple sub-networks. In many sensor networks, signals observed are associated\nwith nodes (i.e., sensors), and edges of the network represent the inter-node\nconnectivity. For a large sensor network, measuring signal values at all nodes\nover time requires huge resources, particularly in terms of energy consumption.\nTo alleviate the issue, we consider a scenario that a sub-network, i.e.,\ncluster, from the whole network is extracted and an intra-cluster analysis is\nperformed based on the statistics in the cluster. The statistics are then\nutilized to estimate signal values in another cluster. This leads to the\nrequirement for transferring a set of parameters of the sub-network to the\nothers, while the numbers of nodes in the clusters are typically different. In\nthis paper, we propose a cooperative Kalman filter between two sub-networks.\nThe proposed method alternately estimates signals in time between two\nsub-networks. We formulate a state-space model in the source cluster and\ntransfer it to the target cluster on the basis of optimal transport. In the\nsignal estimation experiments of synthetic and real-world signals, we validate\nthe effectiveness of the proposed method.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time-Varying Graph Signal Estimation among Multiple Sub-Networks\",\"authors\":\"Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka\",\"doi\":\"arxiv-2409.10915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an estimation method for time-varying graph signals among\\nmultiple sub-networks. In many sensor networks, signals observed are associated\\nwith nodes (i.e., sensors), and edges of the network represent the inter-node\\nconnectivity. For a large sensor network, measuring signal values at all nodes\\nover time requires huge resources, particularly in terms of energy consumption.\\nTo alleviate the issue, we consider a scenario that a sub-network, i.e.,\\ncluster, from the whole network is extracted and an intra-cluster analysis is\\nperformed based on the statistics in the cluster. The statistics are then\\nutilized to estimate signal values in another cluster. This leads to the\\nrequirement for transferring a set of parameters of the sub-network to the\\nothers, while the numbers of nodes in the clusters are typically different. In\\nthis paper, we propose a cooperative Kalman filter between two sub-networks.\\nThe proposed method alternately estimates signals in time between two\\nsub-networks. We formulate a state-space model in the source cluster and\\ntransfer it to the target cluster on the basis of optimal transport. In the\\nsignal estimation experiments of synthetic and real-world signals, we validate\\nthe effectiveness of the proposed method.\",\"PeriodicalId\":501034,\"journal\":{\"name\":\"arXiv - EE - Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.10915\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10915","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一种在多个子网络中估算时变图信号的方法。在许多传感器网络中,观测到的信号与节点(即传感器)相关联,网络的边代表节点间的连接性。为了缓解这一问题,我们考虑的方案是从整个网络中提取一个子网络(即簇),并根据簇内的统计数据进行簇内分析。然后利用这些统计数据来估计另一个簇中的信号值。这就需要将子网络的一组参数传输给其他子网络,而各集群中的节点数量通常是不同的。本文提出了一种两个子网络之间的合作卡尔曼滤波法。我们在源集群中建立了一个状态空间模型,并在最优传输的基础上将其传输到目标集群。在合成信号和实际信号的估计实验中,我们验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time-Varying Graph Signal Estimation among Multiple Sub-Networks
This paper presents an estimation method for time-varying graph signals among multiple sub-networks. In many sensor networks, signals observed are associated with nodes (i.e., sensors), and edges of the network represent the inter-node connectivity. For a large sensor network, measuring signal values at all nodes over time requires huge resources, particularly in terms of energy consumption. To alleviate the issue, we consider a scenario that a sub-network, i.e., cluster, from the whole network is extracted and an intra-cluster analysis is performed based on the statistics in the cluster. The statistics are then utilized to estimate signal values in another cluster. This leads to the requirement for transferring a set of parameters of the sub-network to the others, while the numbers of nodes in the clusters are typically different. In this paper, we propose a cooperative Kalman filter between two sub-networks. The proposed method alternately estimates signals in time between two sub-networks. We formulate a state-space model in the source cluster and transfer it to the target cluster on the basis of optimal transport. In the signal estimation experiments of synthetic and real-world signals, we validate the effectiveness of the proposed method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信