Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 70, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3059309
Keywords
Indoor locating; microwave imaging; occupant counting; radio frequency identification (RFID); radio wave propagation; reconstruction algorithms
Funding
- United States Department of Energy (DoE) under the Advanced Research Projects Agency-Energy (ARPA-E) SENSOR Project [DE-AR0000946]
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The article presents an indoor device-free object detection system using commercial radio frequency identification technology and passive tags to generate reflectivity images for object recognition. Through testing with a 1:6 room model and real-scale rooms, successful occupant recognition and varying levels of precision in locating objects were achieved.
Indoor device-free object detection has many applications, such as assisted living, home security, and occupant-centered building utility control. To accommodate 3-D room layout variations and improve accuracy, multiple observation units with spatial diversity are often required, which will unavoidably cause a significant increase in the component and deployment cost if each unit needs wired power or battery. In this article, we present an indoor device-free object detection system implemented by a commercial radio frequency identification reader and many passive tags around the room. The passive tag as a dispersed observation point offers a cost-effective solution for the necessary spatial diversity. The tag backscattering phase is assembled to generate the reflectivity image inside the capture volume using Fourier-based reconstruction. A new calibration technique is proposed to compensate for multiplicative path losses and subtract the effect of background clutters, such as furniture. Noisy channels and channels with their line-of-sight blocked by the target are also filtered out during postprocessing to improve locating accuracy and mitigate ambiguity. A 1:6 room model was first used to study the effects of the indoor materials and room layout, where occupant recognition and centimeter-level locating were successfully demonstrated. Real-scale rooms were then tested, where decimeter-level 3-D location error was achieved for a single occupant, and useful information for occupant posture can also be evaluated.
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