Gaussian decomposition is the most commonly used methods for waveform analysis, which is a key post-processing step for the applications of space-borne LiDAR data. However, it usually fails to detect the overlapping pulses of large-footprint waveform data. Therefore, a Gaussian progressive decomposition method based on wavelet transform was proposed in this study to address this issue and applied to Ice, Cloud, and land Elevation Satellite / Geoscience Laser Altimeter System (ICESat/GLAS) data. The new proposed method mainly consists of three key steps. First, the wavelet transform was adopted to detect the target features and estimate the component feature parameters, then the Gaussian model was established to optimize the feature parameters. Second, a new component was added if the fitting accuracy didn’t meet the requirements. Finally, waveform decomposition based on wavelet transform was completed until no more new components were added. Additionally, a comparison experiment between the new proposed method and the Gaussian decomposition method based on inflection point was also conducted to verify the reliability of the new proposed algorithm. Experiment results indicated that our new proposed algorithm can detect twice targets as many as the method based on inflection point, and effectively decompose the targets from overlapping waveforms due to high fitting accuracy of above 98%.