测绘通报 ›› 2023, Vol. 0 ›› Issue (3): 5-9.doi: 10.13474/j.cnki.11-2246.2023.0063

• 学术研究 • 上一篇    下一篇

全波形激光雷达改进的降噪方法

石志远, 徐卫明, 孟浩   

  1. 海军大连舰艇学院军事海洋与测绘系, 辽宁 大连 116018
  • 收稿日期:2022-05-30 发布日期:2023-04-04
  • 通讯作者: 徐卫明。E-mail:xmw921@163.com
  • 作者简介:石志远(1998-),男,硕士生,研究方向为激光雷达在海岛礁区域测量应用关键技术。E-mail:15852883015@163.com
  • 基金资助:
    国家自然科学基金(61071006;41871295)

An advanced denoising method for full-waveform LiDAR

SHI Zhiyuan, XU Weiming, MENG Hao   

  1. Department of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, China
  • Received:2022-05-30 Published:2023-04-04

摘要: 在全波形激光雷达信号从发射到接收的过程中,针对受传播介质、扫测距离、物体性质等因素影响产生噪声的问题,本文提出了一种基于经验模态分解、排序熵和小波阈值的降噪改进方法。首先对波形信号进行经验模态分解或本征模态函数(IMF),计算各本征模态函数排序熵的值;然后应用该值计算小波阈值,并构造新的小波阈值函数,再对相应本征模态函数进行小波阈值降噪;最后将结果重新加和,得到降噪后的波形,从而提高不同噪声信号的降噪方法的自适应性。基于数值仿真和实测数据试验,将本文方法与其他降噪方法进行了对比,基于信噪比、波形相关性、均方根误差、平滑度计算归一化指标和综合指标对本文方法进行了评估,归一化信噪比提高10%~20%,其余指标改善5%~40%。因此,本文方法对不同噪声含量的回波信号均有较好的降噪效果,可以解决全波形激光雷达接收波形中存在的噪声问题。

关键词: 全波形激光雷达, 信号降噪, 经验模态分解, 排序熵, 小波阈值

Abstract: In the process of transmitting to receiving a full-waveform LiDAR signal, noise is generated due to the influence of the propagation medium, the scanning distance, the nature of the object and other factors.Aiming at the problem,this paper has put forward a modified denoising method based on empirical mode decomposition (EMD), permutation entropy (PE) and wavelet threshold method (WTM). Firstly, the initial echo signal is decomposed into several intrinsic mode functions (IMF) and then the PE value corresponding to each IMF is computed. Secondly, the PE value is utilized to form the wavelet threshold value, and new threshold function is constructed. Finally, after being denoised by corresponding WTM, all the IMFs are added and the denoised signal is obtained. Features of EMD, PE and WTM are took advantage of, which enables the new method to deal with noised echo signal adaptively. By making data simulation and measured data experiment, the new method is compared with other methods. Normalized indexes and comprehensive index based on signal-noise ratio, waveform relevance, root mean squared error and smoothness indicate that, the new method increase the normalized signal-noise ratio index by 10% to 20%, while the other indexes are advanced by 5% to 40%. It can be concluded that the new method put forward in this article has better effect and is able to deal with echo signal with different levels of noise during use of full-waveform LiDAR.

Key words: full-waveform LiDAR, signal denoising, empirical mode decomposition, permutation entropy, wavelet threshold method

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