激光雷达脉冲回波的波形分解方法是提取其波形参数的重要手段，也为反演目标高度、倾斜度和粗糙度、反射率提供直接的参数来源。针对部分信噪比较差且具有一定混叠程度的脉冲回波，提出一种基于可变分量的参数随机抽样方法的波形分解算法(WDVCM)。该算法以高斯混合函数为优化模型，通过随机产生高斯分量的特征参数以及删减或生成高斯分量等操作，并分别基于能量函数和拟合标准差作为参数优化的判据，从而实现波形的分解及其参数提取。利用该算法对美国国家航空航天局(NASA)的对地观测星载激光雷达(GLAS)一个条带中的4584个原始波形进行了处理分析。结果发现，约99%的WDVCM和97%的NASA拟合波形结果的相关系数均超过0.95，其中两者相关系数差异不超过0.05占98%。同时，WDVCM和NASA拟合波形的标准差系数均值分别为2.21和3.28，约89%的WDVCM拟合波形的标准差系数均小于NASA拟合波形的标准差系数。所得结果表明，WDVCM对混叠高斯波形的拟合效果更好，适用性更强。

The waveform decomposition method of Lidar pulse signal is an important way to extract the waveform parameters, which provides significant data sources for retrieving the elevation, slope, roughness and reflectance of target. A waveform decomposition algorithm on variable component parameter random sampling method (WDVCM) was proposed to process waveforms with poor SNR and certain overlapping. The algorithm regarded the compounded Gaussian function as the optimization model, and achieved the decomposition and extraction of raw waveforms by generating randomly characteristic parameters and deleting or creating Gaussian component, based on the energy function and the standard deviation of fitting as the criterion for parameter optimization. About 4584 raw waveforms in a stripe of Geoscience Laser Altimeter System (GLAS) developed by National Aeronautics and Space Administration (NASA) were processed using the WDVCM. The result indicates that proportions of fitting waveforms originated from WDVCM and NASA with correlation coefficient over 0.95 are 99% and 97% respectively. Wherein, the ratio with the differences of correlation coefficient less than 0.05 is about 98%. The averages of standard deviation coefficient (SDC) of fitting waveforms provided by WDVCM and the NASA are 2.21 and 3.28, and about 89% of SDC of fitting waveforms processed by WDVCM is less than that from NASA. It proves that the WDVCM is more applicable for decomposing overlapping waveforms with better fitting effect.