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.