Hybrid DCNN-Enabled Depolarizing Chipless RFID: Improving Tag Detection Across Varying Lossy Surfaces and Shapes

IF 3.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Nadeem Rather;Roy B. V. B. Simorangkir;Dinesh R. Gawade;John L. Buckley;Brendan O’Flynn;Salvatore Tedesco
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引用次数: 0

Abstract

This paper presents a comprehensive design and implementation approach for robust detection of depolarizing chipless RFID (CRFID) tags. Depolarizing tags are advantageous compared to co-polar CRFID tags due to their improved performance on RF-lossy materials. This work introduces the application of deep learning (DL) regression modelling to a specialised dataset of depolarised Radar Cross Section (RCS) measurements of a custom 3-bit CRFID tag, acquired through an extensive robot-based data acquisition method. A dataset of 12,600 depolarised Electromagnetic (EM) RCS signatures were collected using an automated data acquisition system to train and validate a 1-dimensional Convolutional Neural Network (1D CNN) architecture. A novel hybrid 1D CNN with Bi-LSTM and attention mechanism architecture was also implemented to visualize the model attention and improve detection performance. We present, for the first time reported in literature, a comprehensive design and AI implementation approach for reliably detecting identification (ID) information from depolarized signals. Also, we report the first instance of describing the impact of surface permittivity variations, tag deformations, tilt angles, and read ranges, all integrated into model training for enhanced robustness in detecting ID information. The developed models facilitate real-time identification and recording of objects, enhancing IoT applications in varied environments. It was observed that both models were able to generalize well to given data, with Model-1 achieving a low RMSE of 0.040 (0.66%) on an unseen test dataset. However, the hybrid model reduced the error further by 27.5% with a test RMSE of 0.029 (0.48%).
混合dcnn支持的去极化无芯片RFID:改进标签检测在不同的有损表面和形状
本文提出了一种全面的设计和实现方法,用于去极化无芯片RFID (CRFID)标签的鲁棒检测。与共极性CRFID标签相比,去极化标签具有优势,因为它们在rf损耗材料上的性能有所提高。这项工作将深度学习(DL)回归建模应用于定制3位CRFID标签的去极化雷达横截面(RCS)测量的专门数据集,该数据集通过广泛的基于机器人的数据采集方法获得。使用自动数据采集系统收集了12,600个去极化电磁(EM) RCS特征的数据集,以训练和验证一维卷积神经网络(1D CNN)架构。为了使模型的注意力可视化,提高检测性能,还实现了一种新型的混合1D CNN,该CNN具有Bi-LSTM和注意力机制架构。我们提出了一种综合设计和人工智能实现方法,用于从去极化信号中可靠地检测识别(ID)信息,这是文献中首次报道。此外,我们报告了描述表面介电常数变化,标签变形,倾斜角度和读取范围影响的第一个实例,所有这些都集成到模型训练中,以增强检测ID信息的鲁棒性。开发的模型有助于实时识别和记录对象,增强物联网在各种环境中的应用。可以观察到,两种模型都能够很好地泛化给定的数据,其中模型1在未见过的测试数据集上实现了0.040(0.66%)的低RMSE。然而,混合模型进一步降低了27.5%的误差,测试RMSE为0.029(0.48%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
5.70
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