var cookieName = "pdfddcookie"; // var cookieValue = null; //返回cookie的value值 // if (document.cookie != null && document.cookie != '') { // var cookies = document.cookie.split(';'); //将获得的所有cookie切割成数组 // for (var i = 0; i < cookies.length; i++) { // var cookie = cookies[i]; //得到某下标的cookies数组 // if (cookie.substring(0, cookieName.length + 2).trim() == cookieName.trim() + "=") {//如果存在该cookie的话就将cookie的值拿出来 // cookieValue = cookie.substring(cookieName.length + 2, cookie.length); // break // } // } // } // if (cookieValue != "" && cookieValue != null) {//如果存在指定的cookie值 // return false; // } // else { // // return true; // } // } // function ShowTwo(webUrl){ // alert("22"); // $.funkyUI({url:webUrl,css:{width:"600",height:"500"}}); // } //window.onload = pdfdownloadjudge;
JSIP> Vol.5 No.4, November 2014
Share This Article:
Cite This Paper >>

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

Abstract Full-Text HTML Download Download as PDF (Size:3420KB) PP. 104-111
DOI: 10.4236/jsip.2014.54013    3,087 Downloads   3,542 Views   Citations
Author(s)    Leave a comment
Liming Li, Xiaodong Chai, Shubin Zheng, Wenfa Zhu


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


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.


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

Cite this paper

Li, L. , Chai, X. , Zheng, S. and Zhu, W. (2014) A De-Noising Method for Track State Detection Signal Based on EMD. Journal of Signal and Information Processing, 5, 104-111. doi: 10.4236/jsip.2014.54013.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Zheng, S.B., Lin, J.H. and Lin, G.B. (2007) Maglev Track Long-Wave Irregularity Detection Based on Inertia Method and Its Implementation. Journal of Electronic Measurement and Instrument, 21, 61-65.
[2] Zheng, S.B., Lin, J.H. and Lin, G.B. (2007) High-Speed Maglev Track Long-Wave Irregularity Detection System Design. Chinese Journal of Scientific Instrument, 28, 1781-1786.
[3] Du, H.T. (2000) Long Wavelength Track Irregularity Detection Method of Digital Filter. China Railway Science, 4, 58-64.
[4] Zhu, H.T., Cai, J. and Wang, Z.Y. (2007) Based on the Orbit of Fiber Optic Gyroscope Direction Irregularity Detection System. Microcomputer Information, 23, 268-269.
[5] Boudraa, A.O. and Cexus, E.-C. (2007) EMD-Based Signal Filtering. IEEE Transactions on Instrumentation and Measurement, 56, 2196-2202.
[6] Tan, S.W., Qin, S.R. and Tang, B.P. (2004) The Hilbert-Huang Transform Filtering Properties and Application. Journal of Chongqing University, 27, 9-14.
[7] Zhao, W.W. and Zeng, X.W. (2008) A New Method of EMD De-Noising. Electronic Science and Technology, 5, 30- 32, 36.
[8] Zhu, W.F., Chai, X.D. and Zheng, S.B. (2012) Based on the Integration Filter Displacement Information Acquisition. Instrument Technique and Sensor, 11, 87-90.
[9] Ren, C.H., Xiong, L.X. and Zhao, X.J. (2010) Wavelet Threshold Filtering in Signal Processing, the Application of Fiber Optic Gyroscope. Piezoelectrics & Acoustooptics, 32, 957-959.
[10] Wang, T. (2010) The EMD Algorithm and Its Application in Signal Denoising. Haerbin Engineering University, Haerbin.
[11] Liu, L.J., Shen, Y. and Wang, Y. (2012) Radar Signal Filter Design Base on HHT Method. Control Conference (CCC), 3611-3616.
[12] Antonino-Daviu, J., Roger-Folch, J., Pons-Llinares, J., Pineda-Sanchez, M., Perez, R.B. and Charlton-Perez, C. (2011) Application of the Empirical Mode Decomposition to Condition Monitoring of Damper Bars in Synchronous Motors. Industrial Electronics (ISIE), Gdansk, 27-30 June 2011, 2118-2123.
[13] Yang, G.L., Zhu, Y.Q. and Yu, H.Y. (2010) The Automatic Seismic Signal Denoising Algorithm Based on HHT. Journal of Geodesy and Geodynamics, 3, 39-42.
[14] Zhu, L.P., Liu, A.J. and Wang, H.X. (2011) Based on the Radar Clutter Suppression of HHT. Modern Defense Technology, 6, 185-190.
[15] Gao, Y.C., Sang, E.F. and Liu, B.F. (2007) The Adaptive De-Noising Algorithm Based on Empirical Mode Decomposition. Computer Engineering and Applications, 43, 59-61.
[16] Sun, W.F., Peng, Y.H. and Xu, J.H. (2008) The Laser Noise Signal De-Noising Method Based on EMD. Journal of Shandong University, 38, 121-125.

comments powered by Disqus
JSIP Subscription
E-Mail Alert
JSIP Most popular papers
Publication Ethics & OA Statement
Frequently Asked Questions
Recommend to Peers
Recommend to Library
Contact Us

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.