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has been cited by the following article:

• TITLE: A De-Noising Method for Track State Detection Signal Based on EMD

  AUTHORS: Liming Li, Xiaodong Chai, Shubin Zheng, Wenfa Zhu

  KEYWORDS: Track Irregularity, Signal De-Noising, Empirical Mode Decomposition, Consecutive Mean Square Error

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.5 No.4, October 9, 2014

  ABSTRACT: In the track irregularity detection, the acceleration signals of the inertial measurement unit (IMU) output which with low frequency components and noise, this paper studied a de-noising algorithm. Based on the criterion of consecutive mean square error, a de-noising method for IMU acceleration signals based on empirical mode decomposition (EMD) was proposed. This method can divide the intrinsic mode functions (IMFs) derived from EMD into signal dominant modes and noise dominant modes, then the modes reflecting the important structures of a signal were combined together to form partially reconstructed de-noised signal. Simulations were conducted for simulated signals and a real IMU acceleration signals using this method. Experimental results indicate that this method can efficiently and adaptively remove noise, and this method can better meet the precision requirement.