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水下激光雷达回波信号中往往含有大量散射噪声. 为了能够有效抑制散射噪声, 提高水下激光雷达测距精度, 提出了基于自适应完备噪声经验模态分解(CEEMDAN)与小波阈值相结合的去噪新方法. 首先通过相关系数法对自适应完备噪声经验模态分解得到的本征模态函数(IMF)进行筛选; 然后对筛选后的本征模态函数进行小波阈值去噪, 进一步去除本征模态函数中的噪声成分; 最后将去噪后的本征模态函数进行信号重构得到去噪后信号. 将该方法应用到不同衰减系数水体的强度调制连续光水下测距实验, 使用白色聚氯乙烯(PVC)反射板为探测目标, 在3.75个衰减长度时, 直接采用相关极值确定延时, 测距误差达到19.2 cm; 应用该方法处理后, 测距误差减小到6.2 cm, 有效提高测距精度.
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 3 目标回波信号的波形和频谱图 (a) 波形图; (b) 频谱图
Fig. 3. Waveform and frequency spectrum of target echo signal: (a) Waveform; (b) frequency spectrum.
图 5 去噪后信号的波形(a)和频谱(b)图
Fig. 5. Waveform (a) and frequency spectrum (b) of the signal after denoising.
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
Fig. 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.
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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
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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
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Google Scholar
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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
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Google Scholar
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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
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[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 物理学报 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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