Research on improved evidence theory based on multi-sensor information fusion
Abstract In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the c...
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Nature Portfolio
2021
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oai:doaj.org-article:cbfb133f7b924cfca910612102ac58f62021-12-02T13:41:22ZResearch on improved evidence theory based on multi-sensor information fusion10.1038/s41598-021-88814-32045-2322https://doaj.org/article/cbfb133f7b924cfca910612102ac58f62021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88814-3https://doaj.org/toc/2045-2322Abstract In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the basic probability distribution of each focal element to obtain a new evidence source. Then the concept of credibility is introduced, and the average support of evidence credibility and evidence focal element is used to improve the synthesis rule, so as to obtain the fusion result. Compared with other algorithms, the proposed algorithm can solve the problems existing in DS evidence theory when dealing with highly conflicting evidence to a certain extent, and the fusion results are more reasonable and the convergence speed is faster.Zhen LinJinye XieNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-6 (2021) |
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Medicine R Science Q Zhen Lin Jinye Xie Research on improved evidence theory based on multi-sensor information fusion |
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Abstract In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the basic probability distribution of each focal element to obtain a new evidence source. Then the concept of credibility is introduced, and the average support of evidence credibility and evidence focal element is used to improve the synthesis rule, so as to obtain the fusion result. Compared with other algorithms, the proposed algorithm can solve the problems existing in DS evidence theory when dealing with highly conflicting evidence to a certain extent, and the fusion results are more reasonable and the convergence speed is faster. |
format |
article |
author |
Zhen Lin Jinye Xie |
author_facet |
Zhen Lin Jinye Xie |
author_sort |
Zhen Lin |
title |
Research on improved evidence theory based on multi-sensor information fusion |
title_short |
Research on improved evidence theory based on multi-sensor information fusion |
title_full |
Research on improved evidence theory based on multi-sensor information fusion |
title_fullStr |
Research on improved evidence theory based on multi-sensor information fusion |
title_full_unstemmed |
Research on improved evidence theory based on multi-sensor information fusion |
title_sort |
research on improved evidence theory based on multi-sensor information fusion |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/cbfb133f7b924cfca910612102ac58f6 |
work_keys_str_mv |
AT zhenlin researchonimprovedevidencetheorybasedonmultisensorinformationfusion AT jinyexie researchonimprovedevidencetheorybasedonmultisensorinformationfusion |
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1718392581448007680 |