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CEEMDAN combined wavelet denoising improved the MW cycle slip detection algorithm.

Yingxiang Jiao1, Kezhao Li2,3, Zhe Yue1

  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China.

Scientific Reports
|October 8, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved method for detecting cycle slips in navigation data, reducing noise interference. The new approach enhances accuracy and avoids missed detections, improving positioning reliability.

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Area of Science:

  • Geomatics Engineering
  • Signal Processing

Background:

  • The standard MW combination method for cycle slip detection in high-precision navigation is susceptible to pseudorange noise.
  • This noise leads to missed small cycle slips and threshold divergence after detection failures.

Purpose of the Study:

  • To develop an improved MW combination cycle slip detection method.
  • To mitigate the impact of pseudorange noise and enhance detection accuracy.

Main Methods:

  • Utilizing Complete Ensemble Empirical Mode Decomposition (CEEMDAN) to decompose signals.
  • Employing permutation entropy to select intrinsic mode functions (IMFs) for denoising.
  • Applying wavelet denoising to further eliminate residual noise.

Main Results:

  • The improved method significantly reduces the influence of pseudorange noise.
  • False detection rates decreased from 1.6% to 0% (for 6-0% slips).
  • Successfully detected small period slips in complex noise environments.

Conclusions:

  • The proposed method effectively overcomes the limitations of the original MW algorithm.
  • It enhances the reliability of cycle slip detection in challenging conditions.
  • Avoids missed detections and subsequent threshold divergence issues.