A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms

Abstract

In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this article, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) The deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the Manuscript

A Compensation Method of Saturated Waveform for Space-Borne Laser Altimeter

“…In the satellite on-orbit operation, the quantization error of the space-borne laser analog waveform signal is generated after digital processing, which makes the emission and echo appear as multiple maximum values in some cases or prevents the peak point from being the real waveform peak point, resulting in random errors in laser ranging. Therefore, to improve the ranging accuracy, the least square method is used to iteratively perform Gaussian fitting for the effective waveform, and then the waveform characteristic parameters are extracted [32].…”

Gaussian convolution decomposition for non-Gaussian shaped pulsed LiDAR waveform

“…(c) Using the Gaussian characteristic parameters of the system waveform and the echo, the initial parameter of each component of the target response can be solved by the Gaussian convolution process in equation (15). Figure 2(c) compares the initial estimated components and original components of the target response, and the RMSE = 0.2083 between the estimated and original target response.…”

“…It is of great importance to study the full waveform decomposition technique to extract more echo features. Common methods for full waveform decomposition contain deconvolution [13][14][15] and decomposition [16][17][18]. For the deconvolution method, it is generally accepted that the full waveforms can be represented as the convolution of the transmitted pulse and the target response [19].…”

“…For the deconvolution method, it is generally accepted that the full waveforms can be represented as the convolution of the transmitted pulse and the target response [19]. The deconvolution method, such as B-spline deconvolution [13], Gold method [14], Richardson-Lucy (R-L) iteration [15], can remove the system waveform from the echo. So it can provide the surface response function of the objects, eliminate the blurring of the emitted pulse on the target response, and improve the extract of the target information.…”

“…However, the calculation process is time-consuming. In 2020, Zhang et al [15] adopted four deconvolution methods including two direct solving methods and two iterative methods, then the deconvolution results are used to solve the initial parameter of Gaussian decomposition. This method not only improves the component detection rate, but also has high computational efficiency.…”