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Signal Denoising of Traffic Speed Deflectometer Measurement Based on Partial Swarm Optimization-Variational Mode

Chaoyang Wu1, Yiyuan Duan1, Hao Wang2

  • 1School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

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|June 27, 2024
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Summary

A new method using partial swarm optimization-variational mode decomposition (PSO-VMD) effectively reduces noise in traffic speed deflectometer (TSD) signals. This technique improves the accuracy of deflection data identification for better road condition analysis.

Keywords:
PSOTSDVMDdeflection testdenoising

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Area of Science:

  • Civil Engineering
  • Signal Processing
  • Data Analysis

Background:

  • Traffic speed deflectometers (TSD) collect crucial deflection data for road assessment.
  • Noise in TSD signals can lead to inaccurate identification and analysis of road conditions.
  • Existing denoising methods may not be optimal for TSD-specific signal characteristics.

Purpose of the Study:

  • To propose and validate a novel denoising method for traffic speed deflectometer (TSD) signals.
  • To enhance the accuracy of deflection data identification by reducing measurement noise.
  • To offer a robust strategy for TSD signal denoising.

Main Methods:

  • Variational Mode Decomposition (VMD) for initial signal decomposition.
  • Correlation coefficient calculation for modal selection and signal reconstruction.
  • Particle Swarm Optimization (PSO) to optimize VMD hyperparameters (K and α) using fuzzy entropy.

Main Results:

  • The proposed PSO-VMD method demonstrated superior denoising performance on simulated signals.
  • Optimization of VMD parameters (K and α) was successfully achieved using PSO and fuzzy entropy.
  • Real TSD data denoising confirmed the method's effectiveness in reducing deflection noise.

Conclusions:

  • The PSO-VMD method provides an effective and feasible strategy for TSD signal denoising.
  • Accurate identification of deflection data is improved by minimizing noise.
  • This approach contributes to more reliable road condition assessments using TSD data.