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Study on the Influence of Liquid Level Height in Containers on RFID System Performance 容器液位高度对RFID系统性能影响的研究
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-03 DOI: 10.1109/JRFID.2025.3605595
Lei Zuo;Bihang Lei;Lingshuo Li;Bing Li;Baiqiang Yin;Lifen Yuan
Focusing on the issue of how variations in liquid level height within a container affect the performance of passive ultrahigh frequency (UHF) radio frequency identification (RFID) tags, this study derives a link budget model for a passive UHF RFID system based on RFID operational principles and electromagnetic wave propagation theory. Using power transmission coefficients, the study analyzes how impedance mismatch caused by liquid in the container affects system performance. To validate the theoretical model, a combination of simulations and indoor experiments was employed, establishing segmented models of the tag response signal power (RSSI) as a function of liquid level height in both vertical and horizontal tag orientations. The RSSI of two tags, Alien9662 and Alien9640, was tested in an open indoor environment across varying liquid levels from 0 mm to 140 mm, measuring signal strength variations under different liquid levels. Theoretical analysis and experimental results reveal that when the liquid level changes along the antenna’s bent arm, RSSI decreases significantly (e.g., from –43.4 dBm to –75.6 dBm for the Alien9662 tag in vertical deployment). when the liquid level changes along the small electrical loop, RSSI first increases and then decreases (e.g., from –52.8 dBm to –43.4 dBm for L < 20 mm), exhibiting a nonlinear variation with liquid level height. The RSSI changes observed in both tags align with the segmented models, validating the model’s accuracy. These findings not only provide a theoretical basis for understanding the impact of liquid environments on RFID system performance but also offer a reference for optimizing RFID tag placement in liquid containers, which could support practical applications such as inventory management and liquid level monitoring.
针对容器内液位高度变化对无源超高频(UHF)射频识别(RFID)标签性能的影响问题,基于RFID工作原理和电磁波传播理论,推导了无源超高频RFID系统的链路预算模型。利用功率传输系数分析了容器内液体引起的阻抗失配对系统性能的影响。为了验证理论模型,采用仿真和室内实验相结合的方法,建立标签响应信号功率(RSSI)随液位高度在标签垂直和水平方向上的函数的分段模型。对Alien9662和Alien9640两种标签的RSSI进行了室内开放环境下0 ~ 140 mm不同液位的测试,测量了不同液位下信号强度的变化。理论分析和实验结果表明,当液位沿天线弯曲臂方向变化时,RSSI显著降低(如竖直部署时,Alien9662标签的RSSI从-43.4 dBm降至-75.6 dBm)。当液位沿小回路变化时,RSSI先升高后降低(例如,L < 20 mm时,RSSI从-52.8 dBm降至-43.4 dBm),随液位高度呈非线性变化。在两个标签中观察到的RSSI变化与分割的模型一致,验证了模型的准确性。这些发现不仅为理解液体环境对RFID系统性能的影响提供了理论基础,而且为优化RFID标签在液体容器中的放置提供了参考,可以支持库存管理和液位监测等实际应用。
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引用次数: 0
A Low-Complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-Beam Beamformers 基于低复杂度结构神经网络的宽带多波束形成智能实现
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-26 DOI: 10.1109/JRFID.2025.3602901
Hansaka Aluvihare;Sivakumar Sivasankar;Xianqi Li;Arjuna Madanayake;Sirani M. Perera
True-time-delay (TTD) beamformers can produce wideband squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed that TTD beamformers can be efficiently realized using the elements of the delay Vandermonde matrix (DVM), answering the longstanding beam-squint problem. Thus, building on our work on DVM algorithms, we propose a structured neural network (StNN) to realize wideband multi-beam beamformers using structure-imposed weight matrices and submatrices. The structure and sparsity of the weight matrices and submatrices are shown to reduce the computational complexity of the NN significantly. The proposed StNN architecture has $mathcal {O} boldsymbol {(p L M} log boldsymbol M)$ complexity compared to a conventional fully connected L-layers network with $mathcal {O}(M^{2}L)$ complexity, where M is the number of nodes in each layer of the network, p is the number of sub-weight matrices per layer, and $M gt gt p$ . We show numerical simulations in the 24 to 32 GHz range to demonstrate the numerical feasibility of realizing wideband multi-beam beamformers using the proposed StNN architecture. We also show the complexity reduction of the proposed NN and compare that with fully connected NNs, to show the efficiency of the proposed architecture without sacrificing accuracy. The accuracy of the proposed NN architecture was shown in terms of the mean squared error, which is based on an objective function of the weight matrices and beamformed signals of antenna arrays, while also normalizing nodes. The proposed StNN’s robustness was tested against channel impairments by simulating with Rayleigh fading at different signal-to-noise ratios (SNRs). We show that the proposed StNN architecture leads to a low-complexity NN to realize wideband multi-beam beamformers, enabling a path for reconfigurable intelligent systems.
与依赖频率的FFT波束不同,真时延(TTD)波束成形器可以在模拟和数字信号域中产生宽带无斜视波束。我们之前的工作表明,使用延迟范德蒙矩阵(DVM)的元素可以有效地实现TTD波束形成,解决了长期存在的波束斜视问题。因此,基于我们在DVM算法上的工作,我们提出了一个结构化神经网络(StNN)来实现宽带多波束形成,使用结构施加权矩阵和子矩阵。权重矩阵和子矩阵的结构和稀疏性显著降低了神经网络的计算复杂度。与传统的全连接L层网络的$mathcal {O} boldsymbol {(p L M} log boldsymbol M)$复杂度相比,所提出的StNN架构具有$mathcal {O}(M^{2}L)$复杂度,其中M是网络每层的节点数,p是每层的子权重矩阵数,和$M gt gt p$。我们在24至32 GHz范围内进行了数值模拟,以证明使用所提出的StNN架构实现宽带多波束形成的数值可行性。我们还展示了所提出的神经网络的复杂性降低,并将其与完全连接的神经网络进行比较,以在不牺牲精度的情况下显示所提出架构的效率。基于权矩阵和天线阵列波束形成信号的目标函数,同时也对节点进行归一化,所提出的神经网络架构的准确性用均方误差来表示。通过模拟不同信噪比(SNRs)下的瑞利衰落,测试了所提出的StNN对信道损伤的鲁棒性。我们表明,所提出的StNN架构导致低复杂度的NN实现宽带多波束形成,为可重构的智能系统提供了一条路径。
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引用次数: 0
Securing Heterogeneous Network (HetNet) Communications for Wildfire Management: Mitigating the Effects of Adversarial and Environmental Threats 保护异构网络(HetNet)通信用于野火管理:减轻对抗性和环境威胁的影响
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-22 DOI: 10.1109/JRFID.2025.3601843
Nesrine Benchoubane;Olfa Ben Yahia;William Ferguson;Gürkan Gür;Sumit Chakravarty;Gregory Falco;Gunes Karabulut Kurt
In the face of adverse environmental conditions and cyber threats, robust communication systems for critical applications such as wildfire management and detection demand secure and resilient architectures. This paper presents a novel framework that considers both adversarial factors, building resilience into a heterogeneous network (HetNet)integrating Low Earth Orbit (LEO) satellite constellation with High-Altitude Platform Ground Stations (HAPGS) and Low-Altitude Platforms (LAPS), tailored to support wildfire management operations. Building upon our previous work on secure-by-component approach for link segment security, we extend protection to the communication layer by securing both Radio Frequency (RF)/Free Space Optics (FSO) management and different links. Through a case study, we quantify how environmental stressors impact secrecy capacity and expose the system to passive adversaries. Key findings demonstrate that atmospheric attenuation and beam misalignment can notably degrade secrecy capacity across both short- and long-range communication links, while high-altitude eavesdroppers face less signal degradation, increasing their interception capability. Moreover, increasing transmit power to counter environmental losses can inadvertently improve eavesdropper reception, thereby reducing overall link confidentiality. Our worknot only highlights the importance of protecting networks from these dual threats but also aligns with the IEEE P3536 Standard for Space System Cybersecurity Design, ensuring resilience and the prevention of mission failures.
面对不利的环境条件和网络威胁,用于野火管理和探测等关键应用的强大通信系统需要安全和有弹性的架构。本文提出了一个新的框架,考虑了这两个对抗因素,将弹性构建到一个异构网络(HetNet)中,该网络将低地球轨道(LEO)卫星星座与高海拔平台地面站(HAPGS)和低空平台(LAPS)相结合,为支持野火管理操作量身定制。基于我们之前对链路段安全的组件安全方法的研究,我们通过保护射频(RF)/自由空间光学(FSO)管理和不同的链路,将保护扩展到通信层。通过案例研究,我们量化了环境压力因素如何影响保密能力并使系统暴露于被动对手。主要研究结果表明,大气衰减和波束失调会显著降低短距离和远程通信链路的保密能力,而高空窃听者面临的信号衰减较少,从而提高了他们的拦截能力。此外,增加发射功率以对抗环境损失可能会无意中提高窃听者的接收能力,从而降低整个链路的保密性。我们的工作不仅强调了保护网络免受这双重威胁的重要性,而且还符合IEEE P3536空间系统网络安全设计标准,确保弹性和预防任务失败。
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引用次数: 0
Power Efficient Range Extension Techniques for Cattle Health Monitoring Application 牛健康监测中功率高效范围扩展技术的应用
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-19 DOI: 10.1109/JRFID.2025.3600422
Radhika Raina;Kamal Jeet Singh;Suman Kumar
Monitoring cattle behavior regularly is essential for early detection of illness, stress or unusual activity. Although many cattle health monitoring systems exist in the literature, they often overlook techniques that balance power efficiency with range extension. Thus, this paper proposes Bluetooth Low Energy (BLE) based power efficient range extension techniques. These methods include designing high gain antennas for both the transmitter and receiver, using retransmissions and integrating a Power Amplifier (PA) at the transmitter and a Low Noise Amplifier (LNA) at the receiver. By optimizing the PA’s transmission power and utilizing an LNA, the system achieves a communication range of upto approximately 2.5 km while conserving power. Moreover, a key novelty of this work is the smart power control mechanism that fine tunes the PA’s output at the end node, providing an effective balance between the extended range and reduced power usage- an area that has been largely overlooked in existing BLE based cattle monitoring solutions.
定期监测牛的行为对于早期发现疾病、压力或异常活动至关重要。虽然文献中存在许多牛健康监测系统,但它们往往忽略了平衡功率效率和范围扩展的技术。因此,本文提出了基于低功耗蓝牙(BLE)的功率高效范围扩展技术。这些方法包括为发射机和接收机设计高增益天线,使用重传和在发射机上集成功率放大器(PA)和接收机上集成低噪声放大器(LNA)。通过优化PA的传输功率并利用LNA,系统在节省功率的同时实现了高达约2.5公里的通信范围。此外,这项工作的一个关键新颖之处在于智能功率控制机制,它可以在终端节点微调PA的输出,在扩展范围和减少功耗之间提供有效的平衡——这在现有的基于BLE的牛监测解决方案中很大程度上被忽视了。
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引用次数: 0
Comparison of Neuromorphic Coding for Distributed Wireless Spiking Neural Networks Based on Mutual Information and Energy Efficiency 基于互信息和能量效率的分布式无线尖峰神经网络神经形态编码比较
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-18 DOI: 10.1109/JRFID.2025.3600048
Pietro Savazzi;Anna Vizziello;Fabio Dell’Acqua
Wireless spiking neural networks (WSNNs) enable energy-efficient communication, particularly beneficial for edge intelligence and learning within both terrestrial systems and Earth-space network configurations (beyond 5G/6G). Recent studies have highlighted that distributed wireless SNNs (DWSNNs) perform well in inference accuracy and energy-efficient operation in edge devices, despite the challenges posed by constrained bandwidth and spike loss probability. This makes the technology appealing for wireless sensor networks (WSNs) in space scenarios, where energy limitations are significant. In this paper, we explore neuromorphic impulse radio (IR) transmission methodologies tailored for DWSNNs, investigating various coding algorithms that implement IR modulations. Our assessment employs information-theoretic measures to evaluate performance in terms of transmission efficiency. Moreover, the different neuromorphic coding techniques will be evaluated by considering the energy consumption of edge devices under the same constraints of limited bandwidth and additive white Gaussian noise (AWGN), in order to highlight possible trade-offs between transmission and edge inference requirements.
无线尖峰神经网络(wsnn)可实现高能效通信,尤其有利于地面系统和地球空间网络配置(超过5G/6G)中的边缘智能和学习。最近的研究表明,分布式无线snn (dwsnn)在边缘设备中表现出良好的推理精度和节能运行,尽管带宽受限和尖峰损失概率带来了挑战。这使得该技术对空间场景中的无线传感器网络(wsn)具有吸引力,其中能量限制很重要。在本文中,我们探讨了为dwsnn量身定制的神经形态脉冲无线电(IR)传输方法,研究了实现IR调制的各种编码算法。我们的评估采用信息论的方法来评估传输效率方面的性能。此外,将通过考虑在有限带宽和加性高斯白噪声(AWGN)的相同约束下边缘设备的能量消耗来评估不同的神经形态编码技术,以突出传输和边缘推理要求之间可能的权衡。
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引用次数: 0
Aerodynamic Antenna Array for 5.8 GHz UAV Wireless Power Applications 5.8 GHz无人机无线电源应用气动天线阵列
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-18 DOI: 10.1109/JRFID.2025.3599976
Vinicius Uchoa Oliveira;Ricardo A. M. Pereira;Amit Kumar Baghel;Nuno B. Carvalho
Wireless power transfer (WPT) has the potential to supply energy to various applications, such as electric vehicles and uncrewed aerial vehicles (UAVs), enabling extended operation without direct physical connections. This article presents the design, simulation, and experimental validation of a patch antenna array optimized for RF power reception in UAVs, based on a traditional antenna array. To improve aerodynamic performance, structural modifications, such as holes and slits, were introduced to facilitate airflow while maintaining the electromagnetic integrity of the antenna. This new antenna was manufactured and evaluated in an anechoic chamber, achieving a measured gain of 16.6 dBi, closely matching the simulated 17.74 dBi for a $4{times }4$ patch array. Additionally, computer fluid dynamics simulations were performed and the stream trace and drag coefficients were compared for both antennas, confirming that the design reduces drag and enhances stability, making it a viable solution for UAV applications.
无线电力传输(WPT)有可能为各种应用提供能源,例如电动汽车和无人驾驶飞行器(uav),可以在没有直接物理连接的情况下实现扩展操作。本文在传统天线阵列的基础上,提出了一种针对无人机射频功率接收优化的贴片天线阵列的设计、仿真和实验验证。为了提高空气动力学性能,设计人员对天线进行了结构改造,如孔洞和狭缝,以促进气流流动,同时保持天线的电磁完整性。这种新型天线在消声室中制造和评估,实现了16.6 dBi的测量增益,与$4{times}4$贴片阵列的模拟17.74 dBi非常匹配。此外,进行了计算机流体动力学模拟,比较了两种天线的流迹和阻力系数,证实了该设计减少了阻力,提高了稳定性,使其成为无人机应用的可行解决方案。
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引用次数: 0
Deep Learning for Robotic RFID-Localization 机器人rfid定位的深度学习
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-14 DOI: 10.1109/JRFID.2025.3598860
Dimitrios Kapsos;Athanasios Konstantis;Stavroula Siachalou;Aggelos Bletsas;Antonis G. Dimitriou
This paper presents different deep learning architectures that successfully solve the problem of localization of RFID tags by a single antenna on top of a robot in 2D space. Phase measurements, collected by an RFID reader on top of a moving robot, combined with the corresponding antenna-positions, are properly structured, as proposed herein, to form the input vector of different Multilayer Machine Learning Networks. The proposed architectures are originally tested in simulated data, suffering by zero-mean Gaussian noise, achieving centimeter-level accuracy, verifying the soundness of the proposed approach. Subsequently, the models are tested on experimental data involving hundreds of RFID tags and experiments, dividing the dataset into two disjoint sets, the training set and the test set. The proposed deep learning solutions outperformed a maximum-likelihood estimator, since the latter assumes only the effects of the Line-Of-Sight link, while Neural Networks (NNs) identify patterns resulting from all contributions. To the best of our knowledge, this is the first paper that proposes a way to restructure phase measurements collected by a moving robot in a manner that can then be solved by different Machine Learning architectures. The proposed methods provide a scalable and computationally efficient alternative for real-time RFID localization tasks, which can be expanded in 3D space.
本文介绍了不同的深度学习架构,这些架构成功地解决了在二维空间中通过机器人顶部的单个天线定位RFID标签的问题。相位测量由移动机器人顶部的RFID读取器收集,并结合相应的天线位置,如本文所建议的那样,适当地结构化,以形成不同多层机器学习网络的输入向量。所提出的架构最初在模拟数据中进行了测试,受到零均值高斯噪声的影响,达到了厘米级的精度,验证了所提出方法的合理性。随后,在涉及数百个RFID标签和实验的实验数据上对模型进行测试,将数据集分为两个不相交的集,即训练集和测试集。所提出的深度学习解决方案优于最大似然估计器,因为后者仅假设视距链接的影响,而神经网络(nn)识别所有贡献产生的模式。据我们所知,这是第一篇提出了一种方法来重构移动机器人收集的相位测量值的论文,这种方法可以通过不同的机器学习架构来解决。所提出的方法为实时RFID定位任务提供了一种可扩展和计算效率高的替代方案,可以在3D空间中扩展。
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引用次数: 0
Massive MIMO Beam ID-Based Positioning Method With Low Earth Orbit Satellite Mega Constellations 基于大规模MIMO波束id的低地球轨道卫星巨型星座定位方法
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-12 DOI: 10.1109/JRFID.2025.3598214
Mahmoud Elsanhoury;Janne Koljonen;Fabricio S. Prol;Mohammed S. Elmusrati;Heidi Kuusniemi
The growth of satellite-based positioning methods has revolutionized global navigation by providing reliable geolocation capabilities. However, traditional Global Navigation Satellite Systems (GNSS) are increasingly vulnerable to threats like jamming, spoofing, and interception, undermining their reliability in critical applications such as in-flight navigation and emergency services. To address these challenges, Low Earth Orbit (LEO) satellite constellations have emerged as a promising complement to GNSS infrastructure. LEO satellites, orbiting at lower altitudes with higher density, offer improved signal availability, reduced degradation, and better reception on Earth. This paper presents a LEO satellite-based positioning method via massive multiple-input multiple-output (mMIMO) beamforming antennas. The proposed technique not only mitigates GNSS vulnerabilities but also introduces a passive sensing mechanism that facilitates positioning without complex timing synchronization, improving resilience in jamming-prone environments. By utilizing LEO satellite beam identifiers as geographic pointers, our method enables precise positioning through LEO satellite ephemeris and beam pattern data. We validate this beam-based method through simulations, LEO constellation data, vehicular drive-test datasets, and probabilistic positioning models. Positioning results from the first dataset show a mean absolute error (MAE) of 9.15 meters and a 95th percentile error (p95%) of 19.07 meters when combining LEO satellite data with inertial motion data from a moving vehicle. Meanwhile, GNSS accuracy was MAE = 26.6 meters and p95% = 56.6 meters. The second dataset showed consistent results with accuracy improvements in MAE from 18.55 to 9.42 meters, RMSE from 22.24 to 12.05 meters, and p95% from 36.38 to 21.18 meters, compared to GNSS. These findings highlight the potential of LEO satellite positioning to improve accuracy and reliability in challenging environments, with implications for critical applications such as remote sensing, emergency response, search and rescue, and situational awareness.
卫星定位方法的发展通过提供可靠的地理定位能力,彻底改变了全球导航。然而,传统的全球导航卫星系统(GNSS)越来越容易受到干扰、欺骗和拦截等威胁,从而破坏了其在飞行导航和应急服务等关键应用中的可靠性。为了应对这些挑战,低地球轨道(LEO)卫星星座已经成为全球导航卫星系统基础设施的一个有希望的补充。低轨道卫星的轨道高度较低,密度较高,可以提供更好的信号可用性,减少退化,并在地球上获得更好的接收。提出了一种基于低轨道卫星的大规模多输入多输出(mMIMO)波束形成天线定位方法。所提出的技术不仅减轻了GNSS的漏洞,而且还引入了一种被动感知机制,使定位无需复杂的定时同步,从而提高了在容易干扰的环境中的恢复能力。通过利用LEO卫星波束标识符作为地理指针,我们的方法可以通过LEO卫星星历和波束模式数据进行精确定位。我们通过仿真、LEO星座数据、车辆驾驶测试数据集和概率定位模型验证了这种基于波束的方法。第一个数据集的定位结果显示,将LEO卫星数据与移动车辆的惯性运动数据相结合,平均绝对误差(MAE)为9.15米,第95百分位误差(p95%)为19.07米。同时,GNSS精度MAE = 26.6 m, p95% = 56.6 m。与GNSS相比,第二个数据集的MAE精度从18.55米提高到9.42米,RMSE从22.24米提高到12.05米,p95%从36.38米提高到21.18米。这些发现突出了低轨道卫星定位在具有挑战性的环境中提高准确性和可靠性的潜力,对遥感、应急响应、搜索和救援以及态势感知等关键应用具有重要意义。
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引用次数: 0
Dual-Channel FMCW Harmonic Radar With Active Tags at 61/122 GHz for Phase-Based Gait Parameter Monitoring 61/122 GHz有源标签双通道FMCW谐波雷达相位步态参数监测
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-08 DOI: 10.1109/JRFID.2025.3597021
Patrick Kwiatkowski;Steffen Hansen;Alexander Orth;Francisco Geu Flores;Falko Heitzer;Nils Pohl
Optimized rehabilitation after joint replacement surgery or other medical procedures affecting the musculoskeletal system requires practical movement analysis systems that enable the continuous and precise gait monitoring of patients in everyday life. To address this need, we present a system consisting of a frequency-modulated continuous-wave (FMCW) radar sensor and active frequency-doubling tags designed for accurate long-term monitoring. By using a harmonic measurement concept in which the tags double the frequency of the transceiver signal, a high signal-to-interference-and-noise ratio (SINR) is achieved, ensuring that the tags stand out clearly from the clutter produced by the leg. With our system, we particularly focus on a phase-based angle determination within the sagittal plane, enabled by two closely spaced receive antennas, allowing for more accurate and reliable gait monitoring compared to our previous system based on a bilateration method. By utilizing millimeter waves in the frequency range 56-63 GHz for transmission and 112-126 GHz for reception, we achieve a compact sensor size sufficient for the application. Based on measurements taken in a gait laboratory, we demonstrate that our system is capable of measuring the distance and angle between the sensor and tags during gait with an accuracy of up to 1.73 mm and 0.93°, respectively, using a stationary camera-based motion capture (MoCap) system as a reference.
关节置换手术或其他影响肌肉骨骼系统的医疗程序后的优化康复需要实用的运动分析系统,能够在日常生活中对患者进行连续和精确的步态监测。为了满足这一需求,我们提出了一个由调频连续波(FMCW)雷达传感器和有源倍频标签组成的系统,旨在进行精确的长期监测。通过使用谐波测量概念,其中标签将收发信号的频率加倍,实现了高信噪比(SINR),确保标签从腿产生的杂波中清晰地脱颖而出。在我们的系统中,我们特别关注矢状面内基于相位的角度确定,通过两个紧密间隔的接收天线实现,与之前基于双边方法的系统相比,可以更准确、更可靠地监测步态。通过利用56-63 GHz频率范围内的毫米波进行传输,112-126 GHz频率范围内的毫米波进行接收,我们实现了一个紧凑的传感器尺寸,足以满足应用需求。基于在步态实验室进行的测量,我们证明了我们的系统能够在步态过程中测量传感器和标签之间的距离和角度,精度分别高达1.73 mm和0.93°,使用基于固定摄像机的运动捕捉(MoCap)系统作为参考。
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引用次数: 0
A 915 – MHz Differential Rectifier and ASK/OOK Demodulator SoC for RF Energy Harvesting in Battery-Less ESL and IoT Applications 一种915 MHz差分整流器和ASK/OOK解调器SoC,用于无电池ESL和物联网应用中的射频能量收集
IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-08-04 DOI: 10.1109/JRFID.2025.3595432
Chien-Chin Huang;Hsin Chen
This article presents the design and implementation of a novel receiver system-on-chip (SoC) for an RF energy harvester, which integrates a differential rectifier and a differential ASK/OOK demodulator. The SoC is fabricated using a standard 180 nm CMOS process. Targeted for applications in electronic shelf labels (ESL) and the Internet of Things (IoT), the proposed design operates in the 915 MHz ISM band. An off-chip differential matching network passively enhances the weak RF input signal from the antenna. A limiter circuit is incorporated within the proposed self-compensated differential rectifier to convert the RF signal into dual DC output voltages. The sixstage rectifier enhances the transistor overdrive voltage through dynamic negative biasing a. Furthermore, a novel differential ASK/OOK demodulator provides high-sensitivity detection of RFID signals transmitted from the reader. Measurement results demonstrate a startup sensitivity of -28.48 dBm for a capacitive load at a 1 V DC output, outperforming previously reported designs. The peak end-to-end power conversion efficiency reaches 45.5% at an input power of -2.26 dBm, delivering a load current of $106 mu$ A and an output voltage of 2.53 V.
本文介绍了一种用于射频能量采集器的新型接收器片上系统(SoC)的设计和实现,该系统集成了差分整流器和差分ASK/OOK解调器。该SoC采用标准的180纳米CMOS工艺制造。针对电子货架标签(ESL)和物联网(IoT)的应用,拟议的设计工作在915 MHz ISM频段。片外差分匹配网络被动增强了来自天线的微弱射频输入信号。在所提出的自补偿差分整流器中加入了限制电路,将射频信号转换为双直流输出电压。六级整流器通过动态负偏置a来提高晶体管的过驱动电压。此外,一种新型差分ASK/OOK解调器提供了从读取器传输的RFID信号的高灵敏度检测。测量结果表明,在1 V直流输出的容性负载下,启动灵敏度为-28.48 dBm,优于先前报道的设计。当输入功率为-2.26 dBm时,端到端功率转换效率达到45.5%,负载电流为106 μ $ a,输出电压为2.53 V。
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引用次数: 0
期刊
IEEE journal of radio frequency identification
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