Waveform light detection and ranging detection (LiDAR) systems capture the entire backscattered signal from the interaction of the laser beam with objects located within the laser footprint. The target response (TR) is a time-dependent curve implying the geophysical attribute of the detected objects. TR restoration by removing the effect of the system waveform (SW) from the received waveform is crucial but suffers from ill-posedness. We recast the deconvolution problem to a nonlinear least squares problem and use the proposed method to deal with the LiDAR waveforms with negative tails. The proposed method is an iterative algorithm starting with a practical initial TR seen as the combination of parts of the deconvolution results obtained by the L1- and L2-regularization methods, then ending by a stopping criteria set empirically. A set of hybrid LiDAR waveforms constructed by the SW of our LiDAR and the synthetic TRs are employed to evaluate the performance of the TR retrieval. The results show the superior performance of the proposed method in both reconstructions of the flattened and sharp curves of the TRs as compared to the L1- and L2-regularization methods. This demonstrates the potential of the nonlinear least squares method for retrieving the range and geometric physical information from the LiDAR waveforms.

中文翻译：

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使用非线性最小二乘法对负尾部的光检测和测距检测波形进行去卷积

波形光检测和测距检测（LiDAR）系统从激光束与位于激光足迹内的物体的相互作用捕获整个反向散射信号。目标响应（TR）是随时间变化的曲线，表示检测到的对象的地球物理属性。通过从接收到的波形中消除系统波形（SW）的影响来恢复TR是至关重要的，但存在不适性。我们将反卷积问题重铸为非线性最小二乘问题，并使用所提出的方法处理负尾的LiDAR波形。所提出的方法是一种迭代算法，从实际的初始TR开始，该初始TR被视为通过L1和L2正则化方法获得的反卷积结果的一部分的组合，然后以经验设置的终止标准结束。由我们的LiDAR的SW和合成TR构成的一组混合LiDAR波形用于评估TR检索的性能。结果表明，与L1和L2正则化方法相比，该方法在TRs的平坦曲线和锐利曲线重构中均具有出色的性能。这证明了非线性最小二乘法可用于从LiDAR波形中检索距离和几何物理信息的潜力。