Publication of Cross-Correlation Spectra for Frequency Measurements of Noisy Signals

Publication of Cross-Correlation Spectra for Frequency Measurements of Noisy Signals

We are pleased to share with you our article “Optimization of the Processing Time of Cross-Correlation Spectra for Frequency Measurements of Noisy Signals”, published in Metrology 2022, 2, 293–310 of MDPI, on June 10th, 2022.

Accurate frequency measurement plays an important role in many industrial and robotic systems. However, different influences from the application’s environment cause signal noises, which complicate frequency measurement. In rough environments, small signals are intensively disturbed by noises. Thus, even negative Signal-to-Noise Ratios (SNR) are possible in practice. As a result, frequency measuring methods that can be used for low SNR signals, are in great demand. In previous work, the method of cross-correlation spectrum has been developed as an alternative to Fast Fourier-Transformation or Continuous Wavelet Transformation. It is able to determine the frequencies of a signal under strong noise and is not affected by Heisenberg’s uncertainty principle. However in its current version, its creation is computationally very intensive, thus its application to real-time operations is limited.

Thus in this article, we present a new way to create cross-correlation spectra for frequency measurements of signals with a low signal-to-noise ratio, which aims to reduce processing time to enable real-time application without significant accuracy loss. A new method is developed as well as simulation results are provided in the paper.

Yang Liu, Jigou Liu, and Ralph Kennel serve as authors for this publication.

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