基于PSO-ESPRIT算法的SAW温度传感器解调方法

Demodulation Method for SAW Temperature Sensor Based on PSO-ESPRIT Algorithm

目的 为了提高声表面波(Surface acoustic wave,SAW)温度传感器的测量精度,设计一种基于PSO-ESPRIT算法的高精度SAW温度传感器解调方法。方法 以ESPRIT谱估计方法为基础,把Hankel矩阵的时间窗长度与计算噪声方差时的K值作为粒子群优化(Particle swarm optimization, PSO)算法的输入变量,并以频率估计标准差作为粒子的适应度函数,利用PSO对ESPRIT算法中的参数进行优化,以改善频率估计精度,从而提高SAW回波信号频率估计的分辨率,实现SAW温度传感器的高精度解调。结果 仿真和实验结果表明,所设计的方法与其他谱估计算法相比,其对SAW回波信号估计的频率误差最小,标准差小于0.66kHz。把设计的算法用于SAW温度传感器的温度解调,得到的温度值与实际温度的误差小于0.4℃。结论 测试结果说明,设计的温度解调方法提高了SAW回波信号频率解调精度,可用于SAW温度传感器的解调,实现了对食品包装储运过程中温度的实时监测。

The work aims to design a high-precision demodulation method for surface acoustic wave(SAW) temperature sensor based on the PSO-ESPRIT algorithm, in order to improve the measurement accuracy of SAW sensor. Based on the ESPRIT spectrum estimation method, the time window length of the Hankel matrix and the K value for calculating the noise variance were used as the input variables of the particle swarm optimization(PSO) algorithm. PSO was used to optimize the parameters of ESPRIT algorithm to improve the frequency estimation accuracy, thus increasing the resolution of frequency estimation of SAW echo signal and realizing high-precision demodulation of SAW temperature sensor. Simulation and experimental results indicated that compared with other spectrum estimation algorithms, the designed method had the smallest frequency error in the estimation of SAW echo signals, and the standard deviation was less than 0.66 kHz. When the designed algorithm was used in the temperature demodulation of the SAW temperature sensor, the ror between the obtained temperature value and the actual temperature was less than 0.4 ℃. According to the test results,the designed temperature demodulation method improves the frequency demodulation accuracy of the SAW echo signal,and can be used for the demodulation of the SAW temperature sensor to realize the real-time monitoring of the temperature during the storage and transportation of food packaging.