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Generalized wavelet thresholding technique for optimal noise reduction of Lidar echo signals

Authors:

Daiyin ZhuAuthors Info & Claims

ICSPS 2017: Proceedings of the 9th International Conference on Signal Processing Systems

Pages 140 - 145

https://doi.org/10.1145/3163080.3163098

Published: 27 November 2017 Publication History

Abstract

The wavelet thresholding technique as a multi-scale denoising technique has an outstanding performance in non-stationary signal processing. Currently, this technique was effectively used in noise reduction of Light detection and ranging (Lidar) echo signals. However, the traditional hard and soft thresholding functions lead to the oscillation and detail distortion, respectively, which limits denoising performance. Some revised thresholding functions proposed on the basis of the traditional functions are fixed, which can hardly ensure the optimal performance in different conditions. Therefore, we propose the generalized wavelet thresholding functions and provide the design principle. By adjusting the tuning parameters to compare the signal to noise ratio (SNR) of output in iteration, the optimal performance is finally reached. We apply the proposed functions to the on-site, experimental Lidar data, demonstrate the feasibility of our approach, and derive the optimal noise reduction of the Lidar echo signal, which effectively improves the range accuracy.

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Cited By

Hu BZhao YChen RLiu QWang PZhang Q(2021)Denoising method for a lidar bathymetry system based on a low-rank recovery of non-local data structuresApplied Optics10.1364/AO.43880961:1(69)Online publication date: 21-Dec-2021
https://doi.org/10.1364/AO.438809
Kumar PRamashri T(2020)Noise Reduction in Lidar Signal Based on Sparse Difference MethodCognitive Informatics and Soft Computing10.1007/978-981-15-1451-7_26(235-243)Online publication date: 15-Jan-2020
https://doi.org/10.1007/978-981-15-1451-7_26

Index Terms

Generalized wavelet thresholding technique for optimal noise reduction of Lidar echo signals
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems
      1. Noise reduction

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ICSPS 2017: Proceedings of the 9th International Conference on Signal Processing Systems

November 2017

237 pages

ISBN:9781450353847

DOI:10.1145/3163080

Copyright © 2017 ACM.

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Publication History

Published: 27 November 2017

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Natural Science Foundation of Jiangsu Province

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ICSPS 2017

ICSPS 2017: The 9th International Conference on Signal Processing Systems

November 27 - 30, 2017

Auckland, New Zealand

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Cited By

Hu BZhao YChen RLiu QWang PZhang Q(2021)Denoising method for a lidar bathymetry system based on a low-rank recovery of non-local data structuresApplied Optics10.1364/AO.43880961:1(69)Online publication date: 21-Dec-2021
https://doi.org/10.1364/AO.438809
Kumar PRamashri T(2020)Noise Reduction in Lidar Signal Based on Sparse Difference MethodCognitive Informatics and Soft Computing10.1007/978-981-15-1451-7_26(235-243)Online publication date: 15-Jan-2020
https://doi.org/10.1007/978-981-15-1451-7_26

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