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Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications.

Saiqiang Xia1, Jun Yang1, Wanyong Cai1

  • 1Air Force Early Warning Academy, Wuhan 430019, China.

Sensors (Basel, Switzerland)
|March 3, 2021
PubMed
Summary
This summary is machine-generated.

A novel complex variational mode decomposition (CVMD) method effectively separates micro-motion signals from radar echoes by filtering clutter. This advanced technique offers more robust and accurate signal decomposition than existing methods.

Keywords:
complex variational mode decompositionmicro-motionnarrow-band radaroptimal decomposition layersignal reconstructionsignal separation

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Area of Science:

  • Signal Processing
  • Radar Systems Engineering
  • Adaptive Signal Decomposition

Background:

  • Narrow-band radar echoes contain strong clutter and effective fretting components, necessitating advanced separation techniques.
  • Existing adaptive signal decomposition methods like empirical mode decomposition (EMD) and local mean decomposition (LMD) have limitations in handling complex real-world signals.
  • Variational Mode Decomposition (VMD) is effective for real signals but requires extension for complex-valued radar echo data.

Purpose of the Study:

  • To propose a novel signal processing method, complex variational mode decomposition (CVMD), for enhanced separation of micro-motion signals from radar echoes.
  • To extend the variational mode decomposition (VMD) technique from the real domain to the complex domain to accommodate radar echo characteristics.
  • To improve the accuracy and robustness of signal decomposition and clutter suppression in radar systems.

Main Methods:

  • Extended variational mode decomposition (VMD) to complex domain (CVMD) for processing radar echo signals.
  • Utilized singular value decomposition (SVD) to determine the optimal decomposition order, preventing under- or over-decomposition issues.
  • Employed Mahalanobis distance for robust judgment of mode correlation, outperforming traditional methods like cross-correlation and Euclidean distance.
  • Reconstructed the signal after eliminating weakly correlated modes to isolate the micro-motion component.

Main Results:

  • The proposed CVMD method demonstrated superior performance in filtering strong clutter and fuselage components compared to LMD, EMD, and Moving Target Indicator (MTI) filters.
  • Singular value decomposition (SVD) effectively resolved issues related to decomposition layer selection, providing more accurate results than Detrended Fluctuation Analysis (DFA) and EMD.
  • Mahalanobis distance provided a more robust criterion for mode correlation assessment than Euclidean distance, Bhattacharyya distance, and Hausdorff distance.
  • Successful separation of the micro-motion signal was achieved through local signal reconstruction after mode elimination.

Conclusions:

  • Complex variational mode decomposition (CVMD) is a highly effective method for suppressing clutter and separating micro-motion signals in narrow-band radar echoes.
  • The integration of SVD for optimal decomposition order selection and Mahalanobis distance for mode correlation judgment enhances the accuracy and robustness of the decomposition process.
  • CVMD offers a significant advancement over existing techniques for radar signal processing, particularly in complex clutter environments.