A Support Vector Machine Classification-Based Signal Detection Method in Ultrahigh-Frequency Radio Frequency Identification Systems

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.