Acta Optica Sinica, Volume. 44, Issue 18, 1801002(2024)
Application of Independent Component Analysis Method in Underwater Lidar
Fig. 2. Simulated results of pulse echo signals under different scattering coefficients
Fig. 3. Signals separated by FastICA algorithm. (a) Target echo signal; (b) backscattering noise signal
Fig. 6. Point cloud distance distribution of underwater target. (a)(b) Point cloud distribution before and after processing by FastICA algorithm when turbidity is 4.2 NTU; (c)(d) point cloud distribution before and after processing by FastICA algorithm when turbidity is 12.2 NTU; (e)(f) point cloud distribution before and after processing by FastICA algorithm when turbidity is 20.5 NTU
Fig. 7. Three-dimensional point cloud images of underwater target. (a)(b) Three-dimensional point cloud images before and after processing by FastICA algorithm when turbidity is 4.2 NTU; (c)(d) three-dimensional point cloud images before and after processing by FastICA algorithm when turbidity is 12.2 NTU; (e)(f) three-dimensional point cloud images before and after processing by FastICA algorithm when turbidity is 20.5 NTU
Fig. 8. RMSE of three-dimensional point cloud ranging under different turbidities
Fig. 9. Three-dimensional point cloud images of underwater target when the attenuation coefficient is 2.7 m-1. (a) Unprocessed by FastICA algorithm; (b) processed by FastICA algorithm
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Zhen Wang, Chaoyang Fan, Suhui Yang, Xinyu Liu, Zhen Xu. Application of Independent Component Analysis Method in Underwater Lidar[J]. Acta Optica Sinica, 2024, 44(18): 1801002
Category: Atmospheric Optics and Oceanic Optics
Received: Dec. 22, 2023
Accepted: Mar. 5, 2024
Published Online: Sep. 11, 2024
The Author Email: Yang Suhui (suhuiyang@bit.edu.cn)
CSTR:32393.14.AOS231965