Improved empirical mode decomposition based denoising method for lidar signals

Based on the physical significance of intrinsic mode functions (IMFs) and the noise component removed from the empirical mode decomposition (EMD) method, the denoising process of the lidar (CE370-2, Cimel) backscattering signal is analyzed in detail. Two parameters, typical range (TR) and low-frequency fraction (LFF) are suggested as the reference principles to decide how many high-frequency IMFs should be removed as noise. TR represents the major spatial range of each IMF, which increases with the decrease in the frequency of IMFs; LFF represents the relative value of the low-frequency component of the removed component, which increases as more IMFs are removed. The simulated signals show that the cloud layer altitudes and intensities impact little on the noise reduction processes. Based on an appropriate amount of lidar data, thresholds for TR and LFF are provided, respectively, for various weather conditions: 0.330 and 0.276 for clear sky conditions, 0.460 and 0.517 for cloudy conditions, 0.331 and 0.316 for dusty conditions, and 0.327 and 0.310 for polluted conditions. These thresholds are applied to the automatic data-denoising algorithm. Only 3.9% of the data encounters a numerical calculation error for the clear sky conditions, and the percentage increases to 8.5% for cloudy conditions, which is also acceptable. It turns out that the automatic EMD denoising method has a better denoising performance than that of the wavelet method.

[1]  Bradley Matthew Battista,et al.  Application of the Empirical Mode Decomposition and Hilbert-Huang Transform to Seismic Reflection Data , 2007 .

[2]  A case study of dust aerosol radiative properties over Lanzhou, China , 2010 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[5]  Wei Gong,et al.  Anti-noise algorithm of lidar data retrieval by combining the ensemble Kalman filter and the Fernald method. , 2013, Optics express.

[6]  F. G. Fernald.  Analysis of atmospheric lidar observations: some comments. , 1984, Applied optics.

[7]  J.M.B. Dias,et al.  Time and range averaging of lidar echoes using APD-based receivers , 1999, Optics + Photonics.

[8]  Lei Zhang,et al.  Statistics of aerosol extinction coefficient profiles and optical depth using lidar measurement over Lanzhou, China since 2005–2008 , 2013 .

[9]  C. Weitkamp.  Lidar, Range-Resolved Optical Remote Sensing of the Atmosphere , 2005 .

[10]  Lei Zhang,et al.  An overview of the Semi-arid Climate and Environment Research Observatory over the Loess Plateau , 2008 .

[11]  J. Klett.  Stable analytical inversion solution for processing lidar returns. , 1981, Applied optics.

[12]  Yan-Fang Sang,et al.  Period identification in hydrologic time series using empirical mode decomposition and maximum entropy spectral analysis , 2012 .

[13]  J. Cusido,et al.  Fault detection by means of Hilbert Huang Transform of the stator current in a PMSM with demagnetization , 2010, 2007 IEEE International Symposium on Intelligent Signal Processing.

[14]  Y. X. Huang,et al.  An amplitude-frequency study of turbulent scaling intermittency using Empirical Mode Decomposition and Hilbert Spectral Analysis , 2008, 1401.4211.

[15]  N Menyuk,et al.  Limitations of signal averaging due to temporal correlation in laser remote-sensing measurements. , 1982, Applied optics.

[16]  Stefan Emeis,et al.  Surface-Based Remote Sensing of the Atmospheric Boundary Layer , 2010 .

[17]  G. Papen,et al.  First lidar observations of middle atmosphere temperatures, Fe densities, and polar mesospheric clouds over the north and south poles , 2001 .

[18]  P. T. Woods,et al.  Measurements of toluene and other aromatic hydrocarbons by differential-absorption LIDAR in the near-ultraviolet , 1992 .

[19]  Wei Gong,et al.  Linear segmentation algorithm for detecting layer boundary with lidar. , 2013, Optics express.

[20]  Zhishen Liu,et al.  Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method , 2006 .

[21]  Zhaoyan Liu,et al.  Asian dust transported one full circuit around the globe , 2009 .

[22]  De-Shuang Huang,et al.  Noise reduction in lidar signal based on discrete wavelet transform , 2004 .