The Field-based Model: A New Perspective on RF-based Material Sensing
This paper introduces the design and implementation of WiField, a WiFi sensing system deployed on COTS devices that can simultaneously identify multiple wavelength-level targets placed flexibly. Unlike traditional RF sensing schemes that focus on specific targets and RF links, WiField focuses on all media in the sensing area for the entire electric field. In this perspective, WiField provides a unified framework to finely characterize the diffraction, scattering, and other effects of targets at different positions, materials, and numbers on signals. The combination of targets in different positions, numbers, and sizes is just a special case. WiField proposed a scheme that utilizes phaseless data to complete the inverse mapping from electric field to material distribution, thereby achieving the simultaneous identification of multiple wavelength-level targets at any position and having the potential for deployment on a wide range of low-cost COTS devices. Our evaluation results show that it has an average identification accuracy of over 97% for 1-3 targets (5 cm * 10 cm in size) with different materials randomly placed within a 1.05 m * 1.05 m area.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Review on Deep Learning in UAV Remote Sensing
Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the re…
Deep LearningTime Series AnalysisHuman Centric Embodied Intelligence for Soft Wearable Robotics
Soft wearable robots have evolved rapidly from proof-of-concept devices into promising platforms for rehabilitation, occupational assistance, and human augmentation. As the field matures, its central challenge extends be…
Deep Material Recognition in Light-Fields via Disentanglement of Spatial and Angular Information
Light-field cameras capture sub-views from multiple perspectives simultaneously, with possibly reflectance variations that can be used to augment material recognition in remote sensing, autonomous driving, etc. Existing …
Autonomous DrivingDisentanglementMaterial RecognitionNear-Field Localization and Sensing with Large-Aperture Arrays: From Signal Modeling to Processing
The signal processing community is currently witnessing a growing interest in near-field signal processing, driven by the trend towards the use of large aperture arrays with high spatial resolution in the fields of commu…
Compact Nested Hexagonal Metamaterial Sensor for High-Sensitivity Permittivity Characterization Across S and X-Band Frequencies
This article presents a Compact Nested Hexagonal Metamaterial Sensor designed for microwave sensing to characterize material permittivity in S and X-band applications. The proposed sensor attained compact dimensions of m…
Sensitivity