Frequency Estimation of Sinusoidal Signals Using ASIQ–Rife Interpolation Method

ABSTRACT

To establish a tradeoff between complexity and performance of frequency estimation of sinusoidal signals, a novel automatic segmentation-improved Quinn–Rife method is proposed. The proposed method first utilizes a Discrete Fourier Transform (DFT) algorithm to obtain a coarse estimation, and then divides the frequency deviation into three domains with different processing schemes, which could be suitable for low-delay applications. Simulation results show that the proposed method can overcome the inherent flaws of the classic Quinn and Rife methods, and that the root mean square error closely approaches the Cramer–Rao lower bound at a low signal-to-noise ratio with different estimation ranges. Furthermore, the method does not significantly increase the computational cost under the premise of guaranteed performance, which is beneficial for engineering applications.

KEYWORDS:

• ASIQ–Rife interpolation method
• Cramer–Rao lower bound
• frequency estimation
• Quinn interpolation method
• Rife interpolation method
• sinusoid signals

ACKNOWLEDGMENTS

The authors gratefully the financial support of the National Major Science and Technology Special Project of China [Grant numbers: 2017ZX05009-003].

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Funding

National Major Science and Technology Special Project of China [grant number 2017ZX05009-003].

Notes on contributors

Xinyu Dou

Xinyu Dou received the BS degree in Electronic Information Science and Technology from North China University of Science and Technology, Hebei, China, in 2007. He received the MS degree in Control Theory and Control Engineering from Inner Mongolia University of Technology, Inner Mongolia, China, in 2010. Since 2010, he has been a university lecturer at Tangshan University. Since 2015, he has been pursuing his PhD degree at the College of Geophysics and Information Engineering in China University of Petroleum(Beijing), China. His current research interest is weak signal processing.

E-mail: [email protected]

Huaqing Liang

Huaqing Liang is the corresponding author; she received the BS and MS degrees in Electronic Engineering and Automation Engineering from China University of Petroleum, Beijing, China, in 1986 and 1989, respectively. Since 1989, she has been working at China University of Petroleum, Beijing, China. She received PhD degree in Information Engineering from Beijing University of Posts and Telecommunication, Beijing, China, in 2004. Since 2004, she has been a university professor at China University of Petroleum. Her current research interests are weak signal detection processing, measurement while drilling, and petroleum instruments.

Corresponding author. E-mail: [email protected]