Chen Yifu, Le Yuan, Wu Lin, Li Shuai, Wang Lizhe
State Key Laboratory of Geo-Information Engineering, 1 Yanta Road, Xi'an 710054, China.
School of Computer Science, China University of Geosciences (Wuhan), 388 Lumo Road, Wuhan 430074, China.
Sensors (Basel). 2022 Oct 10;22(19):7681. doi: 10.3390/s22197681.
The single-beam bathymetric light detection and ranging (LiDAR) system 1 (SBLS-1), which is equipped with a 532-nm-band laser projector and two concentric-circle receivers for shallow- and deep-water echo signals, is a lightweight and convenient prototype instrument with low energy consumption. In this study, a novel LiDAR bathymetric method is utilized to achieve single-beam and dual-channel bathymetric characteristics, and an adaptive extraction method is proposed based on the cumulative standard deviation of the peak and trough, which is mainly used to extract the signal segment and eliminate system and random noise. To adapt the dual-channel bathymetric mechanism, an automatic channel-selection method was used at various water depths. A minimum half-wavelength Gaussian iterative decomposition is proposed to improve the detection accuracy of the surface- and bottom-water waveform components and ensure bathymetric accuracy and reliability. Based on a comparison between the experimental results and in situ data, it was found that the SBLS-1 obtained a bathymetric accuracy and RMSE of 0.27 m and 0.23 m at the Weifang and Qingdao test fields. This indicates that the SBLS-1 was bathymetrically capable of acquiring a reliable, high-efficiency waveform dataset. Hence, the novel LiDAR bathymetric method can effectively achieve high-accuracy near-shore bathymetry.
单光束测深激光雷达系统1(SBLS-1)配备了一个532纳米波段的激光投影仪和两个用于接收浅水和深水回波信号的同心圆接收器,是一种轻巧便捷、能耗低的原型仪器。在本研究中,采用了一种新颖的激光雷达测深方法来实现单光束和双通道测深特性,并提出了一种基于峰值和谷值累积标准差的自适应提取方法,主要用于提取信号段并消除系统噪声和随机噪声。为适应双通道测深机制,在不同水深采用了自动通道选择方法。提出了一种最小半波长高斯迭代分解方法,以提高表层和底层水波形分量的检测精度,确保测深精度和可靠性。通过实验结果与现场数据的比较发现,SBLS-1在潍坊和青岛测试场的测深精度和均方根误差分别为0.27米和0.23米。这表明SBLS-1在测深方面能够获取可靠、高效的波形数据集。因此,这种新颖的激光雷达测深方法能够有效地实现高精度近岸测深。