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首页> 外文期刊>IEEE transactions on industrial informatics >A Support Vector Machine Classification-Based Signal Detection Method in Ultrahigh-Frequency Radio Frequency Identification Systems
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A Support Vector Machine Classification-Based Signal Detection Method in Ultrahigh-Frequency Radio Frequency Identification Systems

机译：基于支持向量机分类的超高频射频识别系统中的信号检测方法

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摘要

Noise and distortion in real signals have posed challenges in signal detection in ultrahigh-frequency (UHF) radio frequency identification (RFID) systems, with the exception of large frequency deviation. In this article, signal detection in a UHF RFID system was treated as a time-series classification problem. A support vector machine-based detector was proposed for real-time signal detection to optimize the system performance. The improved system was able to achieve frame synchronization detection and time-series data detection with a frequency deviation of +/- 22%. With the analysis of the frame format, the frame synchronization was based on the sequence detection with the most likely selection, and the data detection was performed using a symbol-by-symbol method with two-step adjustments in order to reduce time and hardware costs. The algorithm was mainly trained offline using the experimental data, and then efficiently implemented in a UHF RFID system based on a software-defined radio platform. The system was validated on real-time signals of commercial tags. The experimental results showed that the learned detector had displayed improvements in detection accuracy and stability when compared to the existing method based on a correlation algorithm.
机译：实际信号中的噪声和失真在超高频率（UHF）射频识别（RFID）系统中引起了信号检测的挑战，除了大的频率偏差。在本文中，UHF RFID系统中的信号检测被视为时间序列分类问题。提出了一种基于支持向量机的检测器，用于实时信号检测以优化系统性能。改进的系统能够实现帧同步检测和时间序列数据检测，频率偏差为+/-22％。随着帧格式的分析，帧同步基于具有最可能选择的序列检测，并且使用具有两步调整的符号符号方法来执行数据检测，以减少时间和硬件成本。该算法主要使用实验数据离线训练，然后在基于软件定义的无线电平台的UHF RFID系统中有效地实现。该系统在商业标签的实时信号上验证。实验结果表明，与基于相关算法的现有方法相比，学习检测器显示出检测精度和稳定性的改善。

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