A Denoising Method for Scanning Lidar Mie Scattering Echo Signal Based on MSW

Removing interferential noise of ground-penetrating radar(GPR) from the echo signal can accurately get the information of underground targets. Raw data reading is a critical step for GPR signal processing. In this paper, the data structure of RIS-K2 is analyzed. Some C++ programs have been written to read the raw data of GPR and the sampling points in form of decimal data are obtained. Then, the data are analyzed and processed with wavelet algorithm in MATLAB. The results show that the noise of the echo can be effectively removed with wavelet denoising method, and it is helpful to determine the underground targets accurately

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Research of Filtering Algorithm for Echo of Ultrasonic Wave Based on Wavelet Transformation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.52-54.1212 ◽

2011 ◽

Vol 52-54 ◽

pp. 1212-1217

Author(s):

Xiao Li Zhu ◽

Ze Zhang

Keyword(s):

Wavelet Transformation ◽

Threshold Function ◽

Wavelet Thresholding ◽

Noise Detection ◽

Matlab Simulation ◽

Noisy Signal ◽

Echo Signal ◽

Simulation Experiments ◽

Denoising Method ◽

Ultrasonic Echo

This paper proposes an improved wavelet threshold denoising method, threshold function by selecting different thresholds to filter out the noisy signal. Firstly, do the wavelet transformation for noisy signal, and then use soft threshold, hard threshold and the improved threshold algorithm to denoising the signal, finally realized by Matlab simulation of wavelet thresholding. Threshold denoising via different methods of simulation experiments show that the improved threshold denoising algorithm can effectively filter plate bonding ultrasonic echo signal in the noise detection and can be a good feature to retain the original signal.

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A Method for Signal Denoising in FMCW Radar Lever Measurement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.373-375.663 ◽

2013 ◽

Vol 373-375 ◽

pp. 663-666

Author(s):

Jian Yun Ni ◽

Jing Luo ◽

Chuan Bin Shan

Keyword(s):

Energy Analysis ◽

Spectral Estimation ◽

Estimation Method ◽

Signal Denoising ◽

Frequency Filtering ◽

Echo Signal ◽

Denoising Method ◽

Signal Energy ◽

Fmcw Radar ◽

Main Component

A novel signal denoising method using Sym3 wavelet in FMCW Radar lever measurement is proposed. The method provided the signal energy distribution display with respect to the particular time and frequency information. Firstly, the main component of echo signal is extracted by energy analysis and scale decomposition by Sym3 wavelet. Then, the disturbed component is eliminated by frequency filtering. Finally, spectral estimation method is used to estimate the relative distance of the object. The simulation results have shown that the Sym3 wavelet was found to be a better approach for denoising in FMCW Radar lever measurement. The Emulation results have shown that measuring accuracy can achieved the 20 millimeter level which can satisfy the desire requirements of the measurement system.

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Feature Extraction of Underwater Target Ultrasonic Echo Based on Wavelet Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1517 ◽

2014 ◽

Vol 599-601 ◽

pp. 1517-1522 ◽

Cited By ~ 1

Author(s):

Zheng Long Wu ◽

Jie Li ◽

Zhen Yu Guan

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Signal Amplitude ◽

Main Task ◽

Echo Signal ◽

Denoising Method ◽

Ultrasonic Echo ◽

Envelope Extraction ◽

Underwater Target ◽

Breaking Points

Ultrasonic detection has been widely used in underwater detectoscopes as an important method for underwater detection. Feature extraction of echo signal time-delay and amplitude is the main task of processing underwater ultrasonic signal. Underwater target ultrasonic echo signal is influenced by reverberation and noise from the sea and system itself, reverberation interference of signal background is the main difficulty for target echo detection. So we use denoising algorithm to denoise echo signal. At first this paper denoises the measured weighted background clutter data using wavelet threshold denoising method, then the paper extracts breaking points of echo signal through wavelet transform, at last the paper makes an envelope extraction using Hilbert transform combined with wavelet transform methods, and acquires the feature information of echo signal amplitude.

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

Atmosphere ◽

10.3390/atmos12111403 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1403

Author(s):

Minghuan Hu ◽

Jiandong Mao ◽

Juan Li ◽

Qiang Wang ◽

Yi Zhang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Mie Scattering ◽

Signal Denoising ◽

Lidar Signal ◽

Measuring Process ◽

Denoising Method ◽

Lidar Return ◽

Deep Learning Neural Network ◽

Better Than

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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Macrostructure and microphysics of Saturn's E-ring

International Astronomical Union Colloquium ◽

10.1017/s0252921100101691 ◽

1984 ◽

Vol 75 ◽

pp. 607-613 ◽

Cited By ~ 2

Author(s):

Kevin D. Pang ◽

Charles C. Voge ◽

Jack W. Rhoads

Keyword(s):

Size Distribution ◽

Spherical Shape ◽

Mie Scattering ◽

Narrow Size Distribution ◽

Electrostatic Levitation ◽

Volcanic Origin ◽

Micron Diameter

Abstract.All observed optical and infrared properties of Saturn's E-ring can be explained in terms of Mie scattering by a narrow size distribution of ice spheres of 2 - 2.5 micron diameter. The spherical shape of the ring particles and their narrow size distribution imply a molten (possibly volcanic) origin on Enceladus. The E-ring consists of many layers, possibly stratified by electrostatic levitation.

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