Fast filter algorithm based on morphological wavelet in precise gravimeter signal processing

被引：0
|
作者：
Zhao L. [1 ,2 ]
Li H. [1 ,2 ]
Zhou B. [1 ,2 ]
Li K. [1 ,2 ]
机构：
[1] School of Instrument Science and Engineering, Southeast University
[2] Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology, Southeast University
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2010年 / 40卷 / 06期
关键词：
Fast Fourier transformation; Gravimeter; Morphological wavelet filter; Signal processing;
D O I：
10.3969/j.issn.1001-0505.2010.06.017
中图分类号：
学科分类号：
摘要：
Combined with the fast Fourier transformation (FFT) method, a fast morphological wavelet filter algorithm is proposed based on the morphological wavelet algorithm and applied to the precise gravimeter signal processing in order to suppress serious background noises and get high precise gravity information. The preliminary work is to add the morphological filter to each level of the classical wavelet decomposition to suppress the impulse noise. Then, the regular wavelet decomposition and reconstruction algorithms are reconstructed. With reference to the FFT method, the fast morphological wavelet decomposition and reconstruction algorithms are designed to increase the computational efficiency. Finally, the performances of the fast morphological wavelet filter and the classical wavelet filter are compared by simulation. Theoretical analysis and simulation results show that the denoising performance of the proposed method is better than that of the classical wavelet algorithm and the computation speed is superior to that of the classical Mallat algorithm.
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页码：1217 / 1221
页数：4
相关论文
共 10 条
  • [1] Xu Z., Yan L., Ning S., Situation and development of marine gravity aided navigation system, Progress in Geophysics, 22, 1, pp. 104-111, (2007)
  • [2] Justin A.R., Gravity gradiometer aided inertial navigation within non-GNSS environments, (2008)
  • [3] Hugh R., Steven K., Louis M., Geophysical navigation technologies and applications, Proceedings of the IEEE Position Location and Navigation Symposium, pp. 618-624, (2004)
  • [4] Nikolaou N.G., Antoniadis I.A., Application of morphological operators as envelop extractors for impulsive-type periodic signals, Mechanical Systems and Signal Processing, 17, 6, pp. 1147-1162, (2003)
  • [5] Sun P., Wu Q.H., An improved morphological approach to background normalization of ECG signals, IEEE Trans Biomed Eng, 50, 1, pp. 117-121, (2003)
  • [6] Maragos P., Schafer R.W., Morphological filters, IEEE Transactions on Acoustics, Speech, and Signal Processing, 35, 8, pp. 1153-1184, (1987)
  • [7] Mallat S., A theory for multi-resolution signal decomposition: The wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 4, pp. 674-693, (1989)
  • [8] Liu F., Ruan X.E., Wavelet-based diffusion approaches for signal denoising, Signal Processing, 87, 5, pp. 1138-1146, (2007)
  • [9] Ji X., Wang S., Xu Y., Application of fast wavelet transformation in signal processing of MEMS gyroscope, Journal of Southeast University: English Edition, 22, 4, pp. 510-513, (2006)
  • [10] Xu W., Song Y., Zong H., A fast wavelet transform algorithm based on discrete Fourier transform, Journal of Nanjing Institute of Technology: Natural Science Edition, 3, 1, pp. 11-17, (2005)
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