Generalized Gaussian decomposition for full waveform LiDAR processing.

Waveform decomposition techniques are commonly used to extract attributes of targets from light detection and ranging (LiDAR) waveforms. Since the shape of a real LiDAR waveform varies for different systems, the conventional models (e.g. the Gaussian function, lognormal function, and generalized normal function) cannot be universally used. In this paper, we present a generalized Gaussian decomposition (GGD) algorithm, which considers the received waveform as the convolution of an arbitrary system waveform with the target response assumed as a Gaussian mixture model. The proposed method was validated using the experimental waveforms sampled from our self-designed LiDAR system with two different system responses. Metrics, including the mean absolute error (MAE) for range retrieval and the root-mean-squared error (RMSE) for waveform fitting, were used to provide a comprehensive quantitative evaluation of the performance. Three classical models for waveform decompositionâ€"the Gaussian, lognormal, and generalized normal functionsâ€"were introduced and studied for the comparison. As for the system waveform with a right-skewed profile, the experimental results showed that the GGD algorithm provided the lowest RMSE for waveform fitting, and the most accurate range estimates with an MAE of 0.030   m . The Gaussian decomposition (GD), lognormal decomposition (LND), and generalized normal decomposition (GND) algorithms produced much worse results with MAEs of 0.362, 1.091, and 0.417   m , respectively. As for the system waveform with a negative tail, the GGD algorithm also performed best with an MAE of 0.019   m , while the GD, LND and GND algorithms provided much larger MAEs of 0.457, 0.489, and 0.354   m , respectively. Therefore, the proposed method has the potential to extract more accurate model parameters from a variety of LiDAR waveforms regardless of the shape of the system waveform. [ABSTRACT FROM AUTHOR]