De-noising Method for Mie Scattering Lidar Echo Signal Based on Wavelet Theory

Zhi-rong, 周智荣 Zhou; Dengxin, 华灯鑫 Hua; Rong, 杨蓉 Yang; Qing, 闫庆 Yan; Hao, 陈浩 Chen; Yuehui, 宋跃辉 Song

doi:10.3788/gzxb20164507.070701002

Cited by 2 publications

(1 citation statement)

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“…The interference noise of the lidar return signal mainly includes background radiation noise, shot noise, dark current noise of the detector, and thermal radiation noise. In order to verify the effectiveness and superiority of the CAENN method, the signal containing gaussian white noise is simulated, also a result comparison of CAENN method with wavelet threshold method and VMD method is performed [26]. In this paper, the Block test signal and Bump test signal proposed by Donoho and Johnstone were superimposed with Gaussian white noise, and the two test signals were denoised by the CAENN, wavelet threshold and VMD methods, respectively [27].…”

Section: Simulation Verificationmentioning

confidence: 99%

A Novel Lidar Signal Denoising Method Based on Convolutional Autoencoding Deep Learning Neural Network

Mao

et al. 2021

Atmosphere

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The lidar is susceptible to the dark current of the detector and the background light during the measuring process, which results in a significant amount of noise in the lidar return signal. To reduce noise, a novel denoising method based on the convolutional autoencoding deep-learning neural network is proposed. After the convolutional neural network was constructed to learn the deep features of lidar signal, the signal details were reconstructed by decoding part to obtain the denoised signal. To verify the feasibility of the proposed method, both the simulated signals and the actually measured signals by Mie-scattering lidar were denoised. Some comparisons with the wavelet threshold denoising method and the variational modal decomposition denoising method were performed. The results show the denoising effect of the proposed method was significantly better than the other two methods. The proposed method can eliminate complex noise in the lidar signal while retaining the complete details of the signal.

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Section: Simulation Verificationmentioning

confidence: 99%