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The echo of underwater lidar often contains a significant quantity of scattering clutters. In order to effectively suppress this scattering clutter and improve the ranging accuracy of underwater lidar, a novel denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising is proposed. The CEEMDAN-wavelet threshold denoising algorithm uses the correlation coefficient to select intrinsic mode function (IMF) components obtained from the CEEMDAN decomposition. The IMFs, which are more closely related to the original signal, are selected. Then, the wavelet thresholding denoising algorithm is applied to each of the selected IMFs to perform additional denoising. For each IMF component, specific threshold values are calculated based on their frequency and amplitude characteristics. Subsequently, the wavelet coefficients of the IMF components are processed by using these threshold values. Finally, the denoised IMF components are combined and reconstructed to obtain the final denoised signal. Applying the wavelet threshold denoising algorithm to IMF components can effectively remove noise components that cannot be removed by traditional CEEMDAN partial reconstruction methods. By using the threshold value calculated based on the characteristics of each IMF component, the wavelet thresholding denoising process is improved in comparison with directly using a single threshold value. This approach enhances the algorithm’s adaptability and enables more effective removal of noise from the signal. We apply the proposed method to underwater ranging experiments. A 532 nm intensity-modulated continuous wave laser is used as a light source. Ranging is performed for a target in water with varying attenuation coefficients. A white polyvinyl chloride (PVC) reflector is used as a target. When the correlation extreme value is directly used to determine the delay at a distance of 3.75 attenuation length, it results in a ranging error of 19.2 cm. However, after applying the proposed method, the ranging error is reduced to 6.2 cm, thus effectively improving the ranging accuracy. These results demonstrate that the method has a significant denoising effect in underwater lidar system. -
Keywords:
- underwater lidar /
- empirical mode decomposition /
- wavelet threshold denoising /
- correlation coefficient method
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图 1 各种阈值函数比较图
Figure 1. Comparison of various threshold functions.
图 2 方法流程图
Figure 2. Method flowchart.
图 3 目标回波信号的波形和频谱图 (a) 波形图; (b) 频谱图
Figure 3. Waveform and frequency spectrum of target echo signal: (a) Waveform; (b) frequency spectrum.
图 4 目标回波信号分解得到的IMF波形图
Figure 4. IMF waveform of target echo signal decomposition.
图 5 去噪后信号的波形(a)和频谱(b)图
Figure 5. Waveform (a) and frequency spectrum (b) of the signal after denoising.
图 6 激光水下探测实验系统
Figure 6. Laser underwater detection experimental system
图 7 不同衰减系数和调制频率下的测距结果 (a), (b) 衰减系数c = 1.5 m–1, 调制频率分别为200 MHz和300 MHz时的测距结果; (c), (d) 衰减系数c = 2.5 m–1, 调制频率分别为200 MHz和300 MHz时的测距结果
Figure 7. Ranging results under different attenuation coefficients and modulation frequencies: (a), (b) Ranging results when the attenuation coefficient is c = 1.5 m–1 and the modulation frequency is 200 MHz and 300 MHz; (c), (d) ranging results when the attenuation coefficient is c = 2.5 m–1 and the modulation frequency is 200 MHz and 300 MHz.
图 8 不同衰减长度下的测量误差
Figure 8. Ranging errors at different attenuation lengths.
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[1] Weiling C, Ke G, Weisi L, Fei Y, En C 2019 IEEE T. Circ. Syst. Vid. 30 334
Google Scholar
[2] Flores N Y, Oswald S B, Leuven R S E W, Collas F P L 2022 Front. Env. Sci. 10 835
Google Scholar
[3] 金鼎坚, 吴芳, 于坤, 李奇, 张宗贵, 张永军, 张文凯, 李勇志, 冀欣阳, 高宇, 李京, 龚建华 2020 红外与激光工程 49 9
Jin D J, Wu F, Yu K, Li Q, Zhang Z G, Zhang Y J, Zhang W K, Li Y Z, Ji X Y, Gao Y, Li J, Gong J H 2020 Infrared Laser Eng. 49 9
[4] Gangelhoff J, Werner C S, Reiterer A 2022 Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2022 Berlin, Germany, November 6–10, 2022 p24
[5] Zhou G Q, Zhou X, Li W H, Zhao D W, Song B, Xu C, Zhang H T, Liu Z X, Xu J S, Lin G C, Deng R H, Hu H C, Tan Y Z, Lin J C, Yang J Z, Nong X Q, Li C Y, Zhao Y Q, Wang C, Zhang L P, Zou L P 2022 Remote Sens. 14 5880
Google Scholar
[6] Liao Y, Yang S, Li K, Hao Y, Li Z, Wang X, Zhang J 2022 IEEE Photonics J. 14 1
Google Scholar
[7] Zha B T, Yuan H L, Tan Y Y 2018 Opt. Commun. 431 81
Google Scholar
[8] Li G Y, Zhou Q, Xu G Q, Wang X, Han W J, Wang J, Zhang G D, Zhang Y F, Yuan Z A, Song S J, Gu S T, Chen F B, Xu K, Tian J S, Wan J W, Xie X P, Cheng G H 2021 Opt. Laser Technol. 142 107234
Google Scholar
[9] Mullen L J, Contarino V M 2000 IEEE Microw. Mag. 1 42
Google Scholar
[10] Pellen F, Guern Y, Cariou J, Lotrian J, Olivard P 2001 J. Phys. D Appl. Phys. 34 1122
Google Scholar
[11] Mullen L, Laux A, Cochenour B 2009 Appl. Opt. 48 2607
Google Scholar
[12] Torres M E, Colominas M A, Schlotthauer G, Flandrin P 2011 2011 IEEE International Conference on Acoustics, Speech, And Signal Processing Prague, Czech Republic, May 22–27, 2011 p4144
[13] Zhang N, Lin P, Xu L 2020 IOP Conference Series: Materials Science and Engineering Sanya, China, December 12–15, 2019 p012073
[14] Gao L, Gan Y, Shi J C 2022 Appl. Intell. 52 10270
Google Scholar
[15] Donoho D L, Johnstone I M 1994 IEEE Transaction on IT 81 425
Google Scholar
[16] 焦新涛 2014 博士学位论文 (广州: 华南理工大学)
Jiao X T 2014 Ph. D. Dissertation (Guangzhou: South China University of Technology
[17] Norden E H, Zheng S, Steven R L, Manli C W, Hsing H S, Quanan Z, Nai-Chyuan Y, Chi C T, Henry H L 1998 P. Roy. Soc. A-Math. Phys. 454 903
Google Scholar
[18] 行鸿彦, 张强, 徐伟 2015 64 040506
Google Scholar
Xing H Y, Zhang Q, Xu W 2015 Acta Phys. Sin. 64 040506
Google Scholar
[19] Wu Z, Huang N E 2009 Adv. Adaptive Data Analysis 1 1
Google Scholar
[20] Abdel-Ouahab B, Jean-Christophe C 2007 IEEE T. Instrum. Meas. 56 2196
Google Scholar
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