Spectrum Fitting Approach for Passive Wireless SAW Sensor Interrogation Using Software-Defined Radio

TL;DR

This paper introduces a new method for measuring frequencies in wireless sensors using software-defined radio, offering faster and more efficient performance.

Contribution

A non-iterative spectrum-fitting method for SAW frequency measurement is proposed, reducing computational complexity while maintaining accuracy.

Findings

The proposed method achieves ±3kHz peak-to-peak accuracy with a 4096-point FFT.

It provides a favorable trade-off between time efficiency and measurement accuracy.

Performance is evaluated through simulations and experiments under various FFT configurations.

Abstract

Passive wireless surface acoustic wave (SAW) sensors are widely adopted for monitoring the safety status of industrial equipment due to their compact size and maintenance-free operation. Replacing traditional discrete-component interrogators with software-defined radio (SDR) architectures offers lower cost and greater flexibility. However, conventional frequency estimation methods often rely on iterative algorithms with high computational complexity, limiting their real-time applicability. This paper presents an SAW sensing system based on an SDR platform and a non-iterative spectrum-fitting method for SAW frequency measurement. The feasibility of the proposed method is theoretically analyzed, and its performance under different window functions and length of fast Fourier transform (FFT) configurations is evaluated through simulations and experimental measurements. The results demonstrate a favorable trade-off between time efficiency and SAW frequency measurement accuracy. Compared to traditional approaches, the proposed method reduces complexity while maintaining ± 3kHz peak-to-peak accuracy with only 4096-point FFT length according to experimental results.

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