###### Abstract  
Airborne bathymetric LiDAR (ABL) acquires waveform data with better accuracy and resolution and greater user control over data processing than discrete returns. The ABL waveform is a mixture of reflections from the water surface and bottom, water column backscattering, and noise, and it can be separated into individual components through waveform decomposition. Because the point density and positional accuracy of the point cloud are dependent on waveform decomposition, an effective decomposition technique is required to improve ABL measurement. In this study, a new progressive waveform decomposition technique based on Gaussian mixture models was proposed for universal applicability to various types of ABL waveforms and to maximize the observation of seafloor points. The proposed progressive Gaussian decomposition (PGD) estimates potential peaks that are not detected during the initial peak detection and progressively decomposes the waveform until the Gaussian mixture model sufficiently represents the individual waveforms. Its performance is improved by utilizing a termination criterion based on the time difference between the originally detected and estimated peaks of the approximated model. The PGD can be universally applied to various waveforms regardless of water depth or underwater environment. To evaluate the proposed approach, it was applied to the waveform data acquired from the Seahawk sensor developed in Korea. In validating the PGD through comparative evaluation with the conventional Gaussian decomposition method, the root mean square error was found to decrease by approximately 70%. In terms of point cloud extractability, the PGD extracted 14-18% more seafloor points than the Seahawk's data processing software. airborne bathymetric LiDAR; progressive waveform decomposition; Gaussian model; Seahawk sensor 26 August 20232225 September 202329 September 20233 October 202326 August 20232225 September 202329 September 20233 October 202322  
accurate three-dimensional (3D) maps [6; 7; 8] and value-added information, such as seafloor roughness [9], suspended sediment concentrations [10], or water turbidity [11]. However, the use of ABL waveform data is limited in practice due to difficulties associated with their analysis. Consequently, the potential of ABL waveform data has not been fully exploited. The LiDAR waveform comprises multiple returns from various targets and non-negligible noise. The simple and traditional method for measuring the target position involves using the waveform to detect peak positions induced by returns from targets. Several methods have been applied, including the center of gravity, local maximum, zero-crossing of the second derivative, and averaged square difference function [12; 13]. However, peak detection methods cannot be utilized to derive valuable properties, such as the amplitude and width of the return pulses, by focusing on the positions of the peaks. Measuring the position and radiation characteristics of each target requires a waveform decomposition process that can separate a continuous signal into individual return signals [14; 15]. Waveform decomposition divides the returned waveform signals from different targets into multiple individual parameterized components. Accordingly, suitable parameterized functions for each returned waveform should be assumed to distinguish the meaningful components accurately. A Gaussian model is generally utilized for waveform decomposition [9; 16]. The approximated parameters (e.g., amplitude, center, and width) of each decomposed Gaussian component are related to the target's physical properties and can be used as waveform features for further applications such as point classification [17; 18; 19] and land-water discrimination [20; 21]. Conventional Gaussian decomposition (CGD) approximates individual waveforms with Gaussian models of the number of previously detected peaks [22].