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Efficient Frequency Estimation of Sinusoid Based on Autocorrelation and Discrete Time Fourier Transform

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Abstract

Frequency estimation of sinusoid is an important research topic in the field of statistical signal processing. Efficient frequency estimation is of great significance for applications with high real-time requirements. In this paper, two efficient frequency estimators of complex sinusoid by using autocorrelation and discrete time Fourier transform (DTFT) are presented in additive white noise background. Firstly, autocorrelation algorithm is adopted to obtain the coarse frequency estimate. Then, three DTFT samples with arbitrary intervals located near the coarse estimate are used to get the fine frequency estimate. In order to evaluate the calculation complexity and estimation accuracy, the proposed estimators are compared with the competing algorithms. The computational complexity of the presented estimators is much lower than that of the competitive estimators. Simulation results show that the mean square errors of the presented estimators are close to the Cramer–Rao lower bound when the signal-to-noise ratio varies in a large range.

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Data Availability

The corresponding author makes available the data presented in this paper upon reasonable request.

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Acknowledgements

This work was supported by the scientific research projects of Liaoning Provincial Department of Education under Grant LJKZ0515.

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Authors and Affiliations

  1. School of Information Science and Engineering, Dalian Polytechnic University, Dalian, 116034, China

    Huanhuan Song, Lei Fan, Huihao Wu & Mingjie Li

  2. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Jinyu Liu & Guoqing Qi

Authors
  1. Huanhuan Song
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  2. Lei Fan
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  3. Jinyu Liu
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  4. Huihao Wu
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  5. Mingjie Li
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Correspondence to Lei Fan.

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Song, H., Fan, L., Liu, J. et al. Efficient Frequency Estimation of Sinusoid Based on Autocorrelation and Discrete Time Fourier Transform. Circuits Syst Signal Process 43, 4675–4689 (2024). https://doi.org/10.1007/s00034-024-02670-w

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  • DOI: https://doi.org/10.1007/s00034-024-02670-w

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