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http://dx.doi.org/10.14372/IEMEK.2022.17.6.355

Parallelized Particle Swarm Optimization with GPU for Real-Time Ballistic Target Tracking  

Yunho, Han (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
Heoncheol, Lee (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
Hyeokhoon, Gwon (PGM R&D Lab, LIGNEX1)
Wonseok, Choi (PGM R&D Lab, LIGNEX1)
Bora, Jeong (PGM R&D Lab, LIGNEX1)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.6, 2022 , pp. 355-365 More about this Journal
Abstract
This paper addresses the problem of real-time tracking a high-speed ballistic target. Particle filters can be considered to overcome the nonlinearity in motion and measurement models in the ballistic target. However, it is difficult to apply particle filters to real-time systems because particle filters generally require much computation time. This paper proposes an accelerated particle filter using graphics processing unit (GPU) for real-time ballistic target tracking. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional particle filter on CPU (central processing unit) showed that the proposed method improved the real-time performance by reducing computation time significantly.
Keywords
Ballistic target tracking; Graphics processing unit; Particle swarm optimization; Real-time systems;
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