用于无源射频感应和感知身体运动的电磁感应生成模型

IF 3.5 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Stefano Savazzi;Federica Fieramosca;Sanaz Kianoush;Michele D’Amico;Vittorio Rampa
{"title":"用于无源射频感应和感知身体运动的电磁感应生成模型","authors":"Stefano Savazzi;Federica Fieramosca;Sanaz Kianoush;Michele D’Amico;Vittorio Rampa","doi":"10.1109/OJAP.2024.3407199","DOIUrl":null,"url":null,"abstract":"Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging and estimation problems, such as passive localization, RF tomography, and holography. Physicsinformed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. They can be used to simulate or reconstruct missing data or samples, reproduce EM propagation effects, approximated EM fields, and learn a physics-informed data distribution, i.e., the Bayesian prior. Generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing, and Artificial Intelligence (AI). The paper discusses two popular techniques, namely Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs), and their adaptations to incorporate relevant EM body diffraction methods. The proposed EM-informed GNN models are verified against classical EM tools driven by diffraction theory, and validated on real data. The paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in communication systems with dense antenna arrays. Proposed tools are also designed, implemented, and verified on resource constrained wireless devices. Simulated and experimental analysis reveal that GNNs can limit the use of time-consuming and privacy-sensitive training stages as well as intensive EM computations. On the other hand, they require hyper-parameter tuning to achieve a good compromise between accuracy and generalization.","PeriodicalId":34267,"journal":{"name":"IEEE Open Journal of Antennas and Propagation","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10542343","citationCount":"0","resultStr":"{\"title\":\"Electromagnetic-Informed Generative Models for Passive RF Sensing and Perception of Body Motions\",\"authors\":\"Stefano Savazzi;Federica Fieramosca;Sanaz Kianoush;Michele D’Amico;Vittorio Rampa\",\"doi\":\"10.1109/OJAP.2024.3407199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging and estimation problems, such as passive localization, RF tomography, and holography. Physicsinformed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. They can be used to simulate or reconstruct missing data or samples, reproduce EM propagation effects, approximated EM fields, and learn a physics-informed data distribution, i.e., the Bayesian prior. Generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing, and Artificial Intelligence (AI). The paper discusses two popular techniques, namely Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs), and their adaptations to incorporate relevant EM body diffraction methods. The proposed EM-informed GNN models are verified against classical EM tools driven by diffraction theory, and validated on real data. The paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in communication systems with dense antenna arrays. Proposed tools are also designed, implemented, and verified on resource constrained wireless devices. Simulated and experimental analysis reveal that GNNs can limit the use of time-consuming and privacy-sensitive training stages as well as intensive EM computations. On the other hand, they require hyper-parameter tuning to achieve a good compromise between accuracy and generalization.\",\"PeriodicalId\":34267,\"journal\":{\"name\":\"IEEE Open Journal of Antennas and Propagation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10542343\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of Antennas and Propagation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10542343/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Antennas and Propagation","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10542343/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

电磁(EM)人体模型可预测人体存在和运动对来自附近无线设备的射频(RF)场的影响。尽管电磁模型非常精确,但它是一种耗时的方法,因此无法用于严格的实时计算成像和估算问题,如被动定位、射频层析成像和全息摄影。物理信息生成神经网络(GNN)模型通过结合相关物理定律和约束条件,具有重现过程的潜力,因此最近吸引了大量关注。它们可用于模拟或重建缺失的数据或样本,重现电磁传播效应、近似电磁场,以及学习物理信息数据分布(即贝叶斯先验)。生成式机器学习是一个将物理/电磁建模、信号处理和人工智能(AI)结合在一起的多学科研究领域。本文讨论了两种流行的技术,即变异自动编码器(VAE)和生成对抗网络(GAN),以及它们的适应性,以纳入相关的电磁体衍射方法。根据由衍射理论驱动的经典电磁工具,对所提出的以电磁为基础的 GNN 模型进行了验证,并在真实数据上进行了验证。论文探讨了 GNN 工具的新机遇,其目标是在具有密集天线阵列的通信系统中进行实时无源射频传感。此外,还在资源受限的无线设备上设计、实施和验证了所提出的工具。模拟和实验分析表明,GNN 可以限制使用耗时和隐私敏感的训练阶段以及密集的电磁计算。另一方面,它们需要进行超参数调整,以实现准确性和泛化之间的良好折衷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electromagnetic-Informed Generative Models for Passive RF Sensing and Perception of Body Motions
Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging and estimation problems, such as passive localization, RF tomography, and holography. Physicsinformed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. They can be used to simulate or reconstruct missing data or samples, reproduce EM propagation effects, approximated EM fields, and learn a physics-informed data distribution, i.e., the Bayesian prior. Generative machine learning represents a multidisciplinary research area weaving together physical/EM modelling, signal processing, and Artificial Intelligence (AI). The paper discusses two popular techniques, namely Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs), and their adaptations to incorporate relevant EM body diffraction methods. The proposed EM-informed GNN models are verified against classical EM tools driven by diffraction theory, and validated on real data. The paper explores emerging opportunities of GNN tools targeting real-time passive RF sensing in communication systems with dense antenna arrays. Proposed tools are also designed, implemented, and verified on resource constrained wireless devices. Simulated and experimental analysis reveal that GNNs can limit the use of time-consuming and privacy-sensitive training stages as well as intensive EM computations. On the other hand, they require hyper-parameter tuning to achieve a good compromise between accuracy and generalization.
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
CiteScore
6.50
自引率
12.50%
发文量
90
审稿时长
8 weeks
×
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