Accurate Sinusoidal Frequency Estimation Algorithm for Internet of Things Based on Phase Angle Interpolation Using Frequency Shift

Frequency estimation of a sinusoidal signal is a fundamental problem in signal processing for the Internet of Things. The frequency interpolation estimation algorithm based on the fast Fourier transform is susceptible to being disturbed by noise, which leads to estimation error. In order to improve the accuracy of frequency estimation, an improved Rife frequency estimation algorithm based on phase angle interpolation is proposed in this paper, namely the PAI-Rife algorithm. We changed the existing frequency deviation factor of the Rife algorithm using phase angle interpolation. Then, by setting the frequency shift threshold, the frequency that is not within the threshold range is shifted to the optimal estimation space. The simulation results show that the proposed algorithm has a wider valid estimation range, and the estimated standard deviation is closer to the Cramer-Rao lower bound. Compared with the Rife algorithm and some recently proposed advanced algorithms, the proposed algorithm has less computational complexity, lower misjudgment rate, and more stable performance.

Automated summary

This paper proposes an improved Rife frequency estimation algorithm based on phase angle interpolation, which improves the accuracy and stability of frequency estimation by changing the frequency deviation factor and setting the frequency shift threshold.