用于下一代物联网和网络物理系统中精确测量和双轴方向检测的全被动式多频透镜 mmID

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Marvin Joshi;Charles A. Lynch;Kexin Hu;Genaro Soto-Valle;Manos M. Tentzeris
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引用次数: 0

摘要

物联网(IoT)的兴起和发展重塑了设备连接和共享信息的方式,带来了更加智能和互联的环境。在这一领域,自持毫米波识别(mmID)设备的应用为提升物联网的实施提供了一个引人注目的机会,尤其是在精确定位和监控能力方面。在这项工作中,作者介绍了一种新颖的透镜式无源毫米波识别(mmID)设备,该设备专为高精度定位和精确的双轴方向检测而量身定制。mmID 配备了频率多样的像素天线阵列,并集成了低损耗三维透镜以提高性能,其单静态 RCS 峰值为 -29.2 dBsm,-10 dB 的角度覆盖范围在所有切面上均为±55°,换算成内径的实角覆盖范围为 2.679 sr。对基于透镜的 mmID 进行了理论链路预算分析,预测当使用 5G/mmWave 频率分配的最大 75 dBm EIRP 时,最大读取距离为 868 米。通过采用 30 dBm EIRP 的概念验证 (PoC) 读取器,所提出的系统实现了高精度定位,在最远 45 米的距离上平均误差小于 2 厘米,并利用灵敏的相位信息,在最远 20 米的距离上实现了基于相位的平均测距误差在 1 毫米以内。此外,该系统还采用了一种新颖的信号处理方法,即多输出分类卷积神经网络(CNN),以准确辨别 mmID 的双轴方位,从而使其在 30 米范围内的平均误差小于 5°。通过提供卓越的精度和多功能性,无源 mmID 解决方案成为下一代 5G/mmWave 网络物理系统(CPS)和物联网应用的一项有前途的进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Fully-Passive Frequency Diverse Lens-Enabled mmID for Precise Ranging and 2-Axis Orientation Detection in Next-Generation IoT and Cyberphysical Systems
The rise and progression of the Internet of Things (IoT) have reshaped how devices connect and share information, leading to more intelligent and interconnected settings. In this realm, the incorporation of self-sustaining millimeter-wave Identification (mmID) devices present a compelling opportunity to elevate IoT implementations, especially concerning accurate positioning and monitoring capabilities. In this work, the authors introduce a novel lens-enabled passive mmID tailored for highly accurate localization and precise 2-axis orientation detection. Equipped with a frequency diverse pixel antenna array and integrated with a low-loss 3D lens for improved performance, the mmID demonstrates a peak monostatic RCS of −29.2 dBsm with a −10 dB angular coverage of ±55° across all cuts, translating to a solid angle coverage of 2.679 sr about boresight. A theoretical link budget analysis is provided for the lens-based mmID, projecting a maximum reading range of 868 m when utilizing the maximum allotted 75 dBm EIRP for 5G/mmWave frequencies. Employing a proof-of-concept (PoC) reader with 30 dBm EIRP, the proposed system demonstrates highly accurate localization, with a mean error of <2 cm at distances up to 45 m, and utilizes sensitive phase information to achieve an average phase-based ranging error within 1 mm across distances up to 20 m. Additionally, a novel signal processing methodology employing multi-output Classification Convolutional Neural Networks (CNN) is introduced to accurately discern the 2-axis orientation of the mmID, resulting in a mean error of <5° at ranges up to 30 m. By offering superior precision and versatility, the passive mmID solution emerges as a promising advancement for next-generation 5G/mmWave Cyber-Physical Systems (CPS) and IoT applications.
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