Full-waveform LiDAR signal filtering based on Empirical Mode Decomposition method

Abstract

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 full-waveform 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.