College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China.
Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China.
Sensors (Basel). 2023 Jun 30;23(13):6058. doi: 10.3390/s23136058.
With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg-Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves.
随着激光雷达技术的快速发展，利用激光雷达进行水下地形探测成为可能。在信号分辨率的过程中仍然存在一个挑战：水下激光回波信号在空气中传播时不同，容易产生弱波和叠加波。然而，现有的波形分解方法在处理这些波形信号时效果不佳，无法正确分解水下波形信号，导致后续数据处理错误。针对这些问题，本研究使用配备 532nm 激光的无人机探测池塘作为研究背景。本文提出了一种改进的拐点选择分解方法来估计参数。通过与其他分解方法进行比较，发现 RMSE 为 2.544，R 为 0.995975，更稳定、更准确。估计参数后，本研究使用振荡粒子群优化（OPSO）和 Levenberg-Marquardt 算法（LM）对估计参数进行优化；最终结果表明，本文方法更接近原始波形。为了验证该方法对复杂波形的处理效果，本文对模拟复杂波形进行分解和优化；最终 RMSE 为 0.0016，R 为 1，分解后的高斯分量可以充分表示原始波形。与其他分解方法相比，该方法在复杂波形分解方面表现更好，特别是在弱波和叠加波方面。
