  Waveform decomposition is needed as a first step in the extraction of various
types of geometric and spectral information from hyperspectral full-waveform
LiDAR echoes. We present a new approach to deal with the "Pseudo-monopulse"
waveform formed by the overlapped waveforms from multi-targets when they are
very close. We use one single skew-normal distribution (SND) model to fit
waveforms of all spectral channels first and count the geometric center
position distribution of the echoes to decide whether it contains
multi-targets. The geometric center position distribution of the
"Pseudo-monopulse" presents aggregation and asymmetry with the change of
wavelength, while such an asymmetric phenomenon cannot be found from the echoes
of the single target. Both theoretical and experimental data verify the point.
Based on such observation, we further propose a hyperspectral waveform
decomposition method utilizing the SND mixture model with: 1) initializing new
waveform component parameters and their ranges based on the distinction of the
three characteristics (geometric center position, pulse width, and
skew-coefficient) between the echo and fitted SND waveform and 2) conducting
single-channel waveform decomposition for all channels and 3) setting
thresholds to find outlier channels based on statistical parameters of all
single-channel decomposition results (the standard deviation and the means of
geometric center position) and 4) re-conducting single-channel waveform
decomposition for these outlier channels. The proposed method significantly
improves the range resolution from 60cm to 5cm at most for a 4ns width laser
pulse and represents the state-of-the-art in "Pseudo-monopulse" waveform
decomposition.

Signal Processing