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An efficient parallelization method of Dempster–Shafer evidence theory based on CUDA

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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 \(\left( r-1\right) \times 2^{2n+1}\), 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 \(\frac{(r-1)2^{n}}{\lceil log_2 r \rceil }\), thereby reducing the time complexity of Dempster’s rule effectively.

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Data Availability

The datasets generated and analyzed in the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research was funded by Application of collaborative precision positioning service for mass users(2016YFB0501805-1) and National Development and Reform Commission integrated data service system infrastructure platform construction project(JZNYYY001). We would like to thank Editage (www.editage.cn) for English language editing.

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Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Kaiyi Zhao, Zeqiu Chen, Jiayao Li, Ruizhi Sun & Gang Yuan

  2. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China

    Li Li

  3. Computer School, Beijing Information Science and Technology University, Beijing, 100101, China

    Li Li

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Correspondence to Ruizhi Sun or Gang Yuan.

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Zhao, K., Li, L., Chen, Z. et al. An efficient parallelization method of Dempster–Shafer evidence theory based on CUDA. J Supercomput 79, 4582–4601 (2023). https://doi.org/10.1007/s11227-022-04810-y

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