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基于蜜獾算法采用变分模态分解的相干多普勒测风激光雷达信号去噪

Coherent Doppler wind lidar signal denoising adopting variational mode decomposition based on honey badger algorithm.

作者信息

Zhou Yilun, Li Lang, Wang Kaixin, Zhang Xu, Gao Chunqing

出版信息

Opt Express. 2022 Jul 4;30(14):25774-25787. doi: 10.1364/OE.461116.

Abstract

Coherent Doppler wind lidar (CDWL) is used to measure wind velocity distribution by using laser pulses. However, the echo signal is easily affected by atmospheric turbulence, which could decrease the effective detection range of CDWL. In this paper, a variation modal decomposition based on honey badger algorithm (VMD-HBA) is proposed and demonstrated. Compared with conventional VMD-based methods, the proposed method utilizes a newly developed HBA to obtain the optimal VMD parameters by iterating the spectrum fitness function. In addition, the Correlation Euclidean distance is applied to identify the relevant mode and used to reconstruct the signal. The simulation results show that the denoising performance of VMD-HBA is superior to other available denoising methods. Experimentally, this combined method was successfully realized to process the actual lidar echo signal. Under harsh detection conditions, the effective detection range of the homemade CDWL system is extended from 13.41 km to 20.61 km.

摘要

相干多普勒测风激光雷达(CDWL)通过激光脉冲来测量风速分布。然而,回波信号很容易受到大气湍流的影响,这会降低CDWL的有效探测范围。本文提出并论证了一种基于蜜獾算法的变分模态分解(VMD-HBA)方法。与传统的基于VMD的方法相比,该方法利用新开发的蜜獾算法通过迭代频谱适应度函数来获得最优的VMD参数。此外,采用相关欧几里得距离来识别相关模态并用于信号重构。仿真结果表明,VMD-HBA的去噪性能优于其他现有的去噪方法。通过实验,成功实现了该组合方法对实际激光雷达回波信号的处理。在恶劣的探测条件下,自制CDWL系统的有效探测范围从13.41千米扩展到了20.61千米。

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