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- Title
An efficient parallelization method of Dempster–Shafer evidence theory based on CUDA.
- Authors
Zhao, Kaiyi; Li, Li; Chen, Zeqiu; Li, Jiayao; Sun, Ruizhi; Yuan, Gang
- Abstract
The Dempster–Shafer (D–S) evidence theory is effective for uncertain reasoning; it does not require advanced information. The theory has been widely used in multi-sensor data fusion. However, the time complexity of fusing r pieces of evidence for n possible events using Dempster's combination rule is r - 1 × 2 2 n + 1 , which is considerable. In addition, none of the existing implementations of Dempster's rule directly utilize the parallel performance of GPUs. In this study, an efficient parallelization method for implementing the D–S evidence theory, based on event-based binary encoding and kernel functions on GPUs, was developed. Theoretical analysis and simulation experiments show that the proposed method achieves a speedup of (r - 1) 2 n ⌈ l o g 2 r ⌉ , thereby reducing the time complexity of Dempster's rule effectively.
- Subjects
DEMPSTER-Shafer theory; MULTISENSOR data fusion; KERNEL functions; PARALLEL algorithms
- Publication
Journal of Supercomputing, 2023, Vol 79, Issue 4, p4582
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-022-04810-y