Denoising differential column image motion lidar signal using singular value decomposition

Zhi Cheng; Xu Jing; Feng He; GuoDong Sun

doi:10.1117/12.2285201

24 October 2017 Denoising differential column image motion lidar signal using singular value decomposition

Zhi Cheng, Xu Jing, Feng He, GuoDong Sun

Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104623O (2017) https://doi.org/10.1117/12.2285201
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

Differential column image motion lidar (DCIM lidar) is a recent turbulence monitor for acquiring atmospheric turbulence profile based on active beacon. By imaging the differential column onto a CCD, DCIM lidar can obtain the Fried’s transverse coherence length (r₀) of different altitudes with a high spatial and temporal resolution. Atmospheric turbulence profile can be recovered from r₀ profile based on the integral relationship between r₀ of spherical wave and the refractive structure constant (C²_n ). In order to ensure the retrieved precision of atmospheric turbulence profile, singular value decomposition (SVD) is used to denoise r₀ profile before inversion. The theory of DCIM lidar and SVD denoising is described. The Hankel matrix is constructed from the noisy signal and then the SVD is used to obtain the singular values. The rank reduction parameter is determined from the sharp variation of singular value curve. The denoised signal can be reconstructed by choosing the bigger singular values according to the rank reduction parameter. The numeric simulations and experiments are both carried out to validate the denoised method of SVD. The results show that the SVD can increase signal-to-noise ratio of r0 profile, thus enhancing the accuracy of the recovered atmospheric turbulence profile.

Citation Download Citation

Zhi Cheng, Xu Jing, Feng He, and GuoDong Sun "Denoising differential column image motion lidar signal using singular value decomposition", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104623O (24 October 2017); https://doi.org/10.1117/12.2285201

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