Qinqin Wu, Shengzhi Qiang, Yuanqing Wang, Shuping Ren
Appl Opt. 2019 Oct 10;58(29):7943-7949. doi: 10.1364/AO.58.007943.
The light detection and ranging (LIDAR) full-waveform echo decomposition method based on empirical mode decomposition (EMD) and the local-Levenberg-Marquard (LM) algorithm is proposed in this paper. The proposed method can decompose the full-waveform echo into a series of components, each of which can be assumed as essentially Gaussian. The original full-waveform echo is decomposed into the intrinsic mode functions (IMFs) and a final residual by using the EMD first. Then, the average period (¯) and corresponding energy densities (EDs) of all IMFs are calculated. A suitable IMF is selected based on the relationship between the EDs of IMFs and the white-noise theoretical spread lines of the 99% confidence-limit level. The components in the full-waveform echo can be detected according to the positions of the maxima of the selected IMF. The initial parameters are estimated by using local-LM fitting. The initial parameters are fitted by global-LM fitting. Compared to the traditional (zero-crossing) ZC method, the proposed method has strong anti-noise performance. It can precisely detect the components and estimate the initial parameters of the components. The proposed method is verified by using the synthetic data; coding LIDAR recorded data; and Land, Vegetation, and Ice Sensor data.
本文提出了一种基于经验模态分解（EMD）和局部Levenberg-Marquardt（LM）算法的光探测与测距（LIDAR）全波形回波分解方法。该方法可将全波形回波分解为一系列分量，每个分量可假定为基本高斯型。首先利用EMD将原始全波形回波分解为本征模态函数（IMF）和一个最终残差。然后，计算所有IMF的平均周期（¯）和相应的能量密度（ED）。根据IMF的ED与99%置信限水平的白噪声理论扩展线之间的关系选择合适的IMF。根据所选IMF最大值的位置检测全波形回波中的分量。利用局部LM拟合估计初始参数。通过全局LM拟合对初始参数进行拟合。与传统的（过零）ZC方法相比，该方法具有较强的抗噪声性能。它可以精确地检测分量并估计分量的初始参数。利用合成数据、编码LIDAR记录数据以及陆地、植被和冰传感器数据对所提方法进行了验证。
