Investigation of Self-Adaptive Hierarchical Wavelet Denosing in Lidar Signal Processing

Abstract

Abstract:

During the signal processing of the Raman backscattered lidar, the wavelet transform is used to reduce the noise. The setting of the threshold is the key factor in the transformation. A new method, the self-adaptive hierarchical denosing with a soft threshold is presented. Comparison of different wavelets and thresholds used in the denosing procedure are also described. Compared with the usual average method, such as detail depression method and global threshold method, the self-adaptive hierarchical denosing method can increase the SNR while with maintaining the character of the peak signal. And even when the clouds exist, the signal still can be identified without much change. The detail recognition and inverting precision are thus improved.

Key words: aerosol, Raman lidar, wavelet denoising, self adaptive hierarchical denosing with a soft threshold

CLC Number:

TN911

CHEN Zhen-yi,LIU Wen-qing,ZHANG Yu-jun,HE Jun-feng,RUAN Jun,CUI Yi-ben,LI Sheng. Investigation of Self-Adaptive Hierarchical Wavelet Denosing in Lidar Signal Processing[J]. Journal of Atmospheric and Environmental Optics, 2012, (2): 147-153.

References

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