A De-Noising Method for Track State Detection Signal Based on the Statistical Characteristic of Noise

Liming Li^*, Xiaodong Chai, Shubin Zheng, Wenfa Zhu

College of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, China.

DOI: 10.4236/jtts.2014.44029                

Abstract

Based on the statistical characteristics analysis of random noise power and autocorrelation function, this paper proposes a de-noising method for track state detection signal by using Empirical Mode Decomposition (EMD). This method is used to noise reduction refactoring for the first Intrinsic Mode Function (IMF) component in accordance with the “random sort-accumulation-average-refactoring" order. Signal autocorrelation function characteristics are used to determine the cut-off point of the dominant mode. This method was applied to test signals and the actual inertial unit signals; the experimental results show that the method can effectively remove the noise and better meet the precision requirement.

Keywords

Track Inspection, Long Wave Irregularity, Empirical Mode Decomposition, De-Noising

Conflicts of Interest

The authors declare no conflicts of interest.

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