Improvement of the signal to noise ratio of Lidar echo signal based on wavelet de-noising technique

Zhou, Zhirong; Hua, Dengxin; Wang, Yufeng; Qing, Yan; Li, Shichun; Li, Yan; Wang, Hongwei

doi:10.1016/j.optlaseng.2013.02.011

Cited by 78 publications

(35 citation statements)

References 18 publications

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“…Moreover, wavelet transform-based de-noising methods have been proposed and applied for better performance of lidar system. For example, Zhou et al adopted a Daubechies wavelet Stein's using unbiased risk estimation (SURE) soft threshold method to validate its feasibility and effectiveness in improving SNR of lidar signal [17]. In order to overcome the Gibbs-like phenomenon brought by hard threshold and loss of high-frequency component by the soft threshold, Yin et al proposed a wavelet improved threshold method which incorporates the advantage of the hard and soft thresholds.…”

Section: Wavelet De-noisingmentioning

confidence: 99%

Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

Wang

2015

Optics and Lasers in Engineering

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Section: Wavelet De-noisingmentioning

confidence: 99%

Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

Wang

2015

Optics and Lasers in Engineering

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“…Wavelet analysis has achieved good effort in stochastic signal processing field because of its good time-frequency analysis ability. Many algorithms have been presented, such as wavelet transform [3,4], wavelet packet transform [12], lifting wavelet transform [3], and wavelet threshold denoising method [13]. Among these methods, the wavelet threshold denoising method has been widely used in various situations [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Wavelet Threshold Denoising Method for Environmental Drift of Fiber Optic Gyro

Zhang¹,

Wang

Gao³

et al. 2016

Mathematical Problems in Engineering

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Fiber optic gyroscope (FOG) is a core component in modern inertial technology. However, the precision and performance of FOG will be degraded by environmental drift, especially in complex temperature environment. As the modeling performance is affected by the noises in the output data of FOG, an improved wavelet threshold value based on Allan variance and Classical variance is proposed for discrete wavelet analysis to decompose the temperature drift trend item and noise items. Firstly, the relationship of Allan variance and Classical variance is introduced by analyzing the drawback of traditional wavelet threshold. Secondly, an improved threshold is put forward based on Allan variance and Classical variance which overcomes the shortcoming of traditional wavelet threshold method. Finally, the innovative threshold algorithm is experimentally evaluated on FOG. The mathematical evaluation results show that the new method can get better signal-to-noise ratio (SNR) and gain the reconstruction signal of the higher correlation coefficient (CC). As an experimental validation, the nonlinear capability of error back propagation neural network (BP neural network) is used to fit the drift trend item and find out the complex relationship between the FOG drift and temperature, and the final processing results indicate that the new denoising method can get better root of mean square error (MSE).

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“…LiDAR emits a beam of laser pulse, that interacts with the object or medium or target under test. LiDAR collect this back-scattered signal and employee it to predict certain property of the object [2], for example the study of high cirrus clouds over equatorial regions [3], high latitude polar stratospheric cloud [3], and interception in forested environments [4][5] etc.…”

Section: Introductionmentioning

confidence: 99%

“…For improving the range resolution, several techniques and strategies has been described in the scientific literature [2], [14][15][16][17], are mostly based on wavelet transform. Motivated by the economy of the wavelet representation, Donoho [15], proposed the wavelet based vaguelette decomposition (WVD) algorithm to solve a special class of liner inverse problem.…”

mentioning

confidence: 99%

“…J. Kalifa et al [16], used the mirror wavelet bases for the deconvolution of an image employing the thresholding estimators that decompose signals in an orthonormal basis. In an approach Zhirong et al [17], proposed a wavelet denoising technique for improvement of the signal to noise ratio of LiDAR echo signal. They proposed an effective denoising technique employing wavelet technique based on a soft threshold to reduce the LiDAR echo signal noise and improve the signal to noise ratio of system.…”

mentioning

confidence: 99%

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Comprehensive study on deconvolution and denoising of LiDAR back-scattered signal

Naik

Sahu

2013

2013 International Conference on Microwave and Photonics (ICMAP)

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Light Detection and Ranging (LiDAR) is an efficient tool for remote sensing and for target detection. This technology has been used for years with a variety of applications. One of the major challenges in the field of LiDAR is the resolution enhancement. Improvement of the resolution may be achieved by applying the deconvolution of the measured signal with the system response function. However, the deconvolution process itself is a noise sensitive process. Recent progresses of signal processing techniques in the area of LiDAR are reviewed. Extensive reviews of such techniques are presented. In this paper the LiDAR system equation, operation, and signal processing techniques like Fourier based deconvolution, regularized deconvolution, and Fourier wavelet based regularized deconvolution, Richardson-Lucy algorithm and non-negative least square method of deconvolution are discussed.

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Improvement of the signal to noise ratio of Lidar echo signal based on wavelet de-noising technique

Cited by 78 publications

References 18 publications

Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

An Innovative Wavelet Threshold Denoising Method for Environmental Drift of Fiber Optic Gyro

Comprehensive study on deconvolution and denoising of LiDAR back-scattered signal

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