CEEMD结合改进小波阈值的激光雷达信号去噪算法

激光雷达远距离回波信号受噪声影响, 严重失真。为了有效去除信号的噪声, 提高回波信号信噪比, 提出一种互补集合经验模态分解(complementary ensemble empirical mode decomposition, CEEMD)结合改进小波阈值的去噪算法。CEEMD可以自适应地分解非线性和非平稳信号, 改进小波阈值函数具有高阶可导特性, 能够克服硬阈值、软阈值函数各自存在的问题。两种方法结合, 可以更有效地去除噪声。首先, 对回波信号进行CEEMD分解, 得到若干固有模态函数(intrinsic mode function, IMF)。其次, 通过相关系数法计算IMF分量与信号的相关系数, 确定相关分量和不相关分量。最后, 对不相关分量使用小波改进阈值法进行去噪, 对相关分量使用粗糙惩罚法进行平滑, 再重构信号。基于实测数据的实验结果表明, 所提算法比CEEMD去噪法和CEEMD结合原改进阈值去噪法, 信噪比分别提升了2.65 dB和0.58 dB。

Laser radar signal denoising algorithm based on CEEMD combined with improved wavelet threshold

The long-distance echo signal of laser radar is affected by noise and is seriously distorted. In order to effectively remove the noise of the signal and improve the signal-to-noise ratio of the echo signal, a denoising algorithm combining complementary ensemble empirical mode decomposition (CEEMD) with an improved wavelet threshold is proposed. CEEMD can adaptively decompose nonlinear and non-stationary signals, and the improved wavelet threshold function has high-order differentiability characteristics, which can overcome the problems of hard threshold and soft threshold functions. The combination of the two methods can remove noise more effectively. Firstly, the echo signal is decomposed by CEEMD to obtain several intrinsic mode function (IMF) components. Secondly, the correlation coefficient between the IMF components and the signal is calculated by the correlation coefficient method. And the relevant and uncorrelated components are determined. Finally, the wavelet improved threshold method is used to denoise the uncorrelated components, and the rough penalty method is used to smooth the correlated components, and then the signal is reconstructed. Experimental results based on measured data show that the proposed algorithm has improved the signal-to-noise ratio by 2.65 dB and 0.58 dB compared with the CEEMD denoising method and CEEMD combined with the original improved threshold denoising method.