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GPU-Accelerated Monte Carlo Simulation for a Single-Photon Underwater Lidar.
- Source :
-
Remote Sensing . Nov2023, Vol. 15 Issue 21, p5245. 15p. - Publication Year :
- 2023
-
Abstract
- The Monte Carlo (MC) simulation, due to its ability to accurately simulate the backscattered signal of lidar, plays a crucial role in the design, optimization, and interpretation of the backscattered signal in lidar systems. Despite the development of several MC models for lidars, a suitable MC simulation model for underwater single-photon lidar, which is a vital ocean remote sensing technique utilized in underwater scientific investigations, obstacle avoidance for underwater platforms, and deep-sea environmental exploration, is still lacking. There are two main challenges in underwater lidar simulation. Firstly, the simulation results are significantly affected by near-field abnormal signals. Secondly, the simulation process is time-consuming due to the requirement of a high number of random processes to obtain reliable results. To address these issues, an algorithm is proposed to minimize the impacts of abnormal simulation signals. Additionally, a graphics processing unit (GPU)-accelerated semi-analytic MC simulation with a compute unified device architecture is proposed. The performance of the GPU-based program was validated using 109 photons and compared to a central processing unit (CPU)-based program. The GPU-based program achieved up to 68 times higher efficiency and a maximum relative deviation of less than 1.5%. Subsequently, the MC model was employed to simulate the backscattered signal in inhomogeneous water using the Henyey–Greenstein phase functions. By utilizing the look-up table method, simulations of backscattered signals were achieved using different scattering phase functions. Finally, a comparison between the simulation results and measurements derived from an underwater single-photon lidar demonstrated the reliability and robustness of our GPU-based MC simulation model. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 21
- Database :
- Academic Search Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 173568310
- Full Text :
- https://doi.org/10.3390/rs15215245