A Fully-Passive Frequency Diverse Lens-Enabled mmID for Precise Ranging and 2-Axis Orientation Detection in Next-Generation IoT and Cyberphysical Systems

Abstract

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.