一种基于奇异值分解的SAW传感器频率估计算法

A SAW Sensor Frequency Estimation Algorithm Based on Singular Value Decomposition

在声表面波(SAW)谐振式无线传感器的频率估计中,该文提出了一种奇异值分解(SVD)与快速傅里叶变换(FFT)相结合的频率估计算法。首先采用重复采样的方法对无线SAW谐振器回波信号进行提取,然后在FFT之前利用SVD法去除回波信号中的白噪声,最后通过高斯曲线拟合法对FFT算出的频率进行校正。运用该算法得到的频率均方误差为1.53×103,而直接用FFT算法均方误差为2.38×10~3,均方误差减小了55%。可见,利用该SVD与FFT相结合的频率估计算法在准确度、稳定性方面都有很大改善,且该算法操作简单,易于实现。

The fast fourier transform（FFT）frequency estimation algorithm combined with the singular value decomposition（SVD）is proposed in this paper,which is used to estimate the frequency of the surface acoustic wave（SAW）resonant sensor.The echo signal of wireless SAW resonator is extracted by the method of repeated sampling.The SVD method is used to remove the white noise in echo signal before FFT,and the Gauss curve fitting method is applied to calibrate the frequency calculated by FFT.The mean square error（MSE）applying the proposed algorithm is 1.53×10-3,while the MSE directly applying FFT algorithm is 2.38×10-3,which is reduced by 55%.Thus,using the frequency estimation algorithm combining the SVD and FFT,the accuracy and stability are greatly improved,and the algorithm is simple to operate and easy to implement.