FULL-WAVEFORM LIDAR SIGNAL FILTERING BASED ON EMPIRICAL MODE DECOMPOSITION METHOD

As a new case of Light Detection and Ranging (LiDAR), full-waveform LiDAR records the complete waveform of backscattered echo of targets in certain time interval using high-speed data acquisition device. Since the full-waveform signal is generally short in length and badly contaminated by noise, it is rather difficult to find a method suitable for the signal filtering. In this paper, the Empirical Mode Decomposition (EMD) was extended to the filtering of fullwaveform LiDAR signal. Aiming at simulation signal, the filtering results of EMD-based filtering method were respectively compared with those deduced from Low-pass filter, Wiener filter and Gaussian smoothing. The filtering results show that the Signal to Noise Improvement Ratio (SNIR) of EMD-based filtering method is biggest in all compared filtering methods. Residual Sum of Squares (RSS) of EMD-based filtering method is just bigger than Wiener filter. Meanwhile, the processing results of different filtering methods were fitting with Gaussian function using Levenberg-Marquardt (LM) method. Based on the compare of fitting parameters accuracy of signal filtered by different filtering methods, EMD-based method is more suitable for the preprocessing of Gaussian fitting. At the last, some typical Geoscience Laser Altimeter System (GLAS) data were filtered and fitted using EMD-based filtering method and Levenberg-Marquardt fitting method. The experimental results suggest that the EMD-based filtering method has well filtering result.