Abstract
Dempster–Shafer evidence theory plays an important role in multi-sensor information fusion and is widely used in the real world. However, fusing the highly conflicting evidences may emerge counter-intuitive results. Recently, researchers found that weighting evidences based on its corresponding credibility and information volume are effective in dealing with the above problem. So it is still an open issue about how to obtain reasonable weights. In this paper, a new divergence measure method is proposed to measure divergence degree of basic probability assignment based on harmonic mean of Deng relative entropy. In determining information volume, Zhou et al.’s entropy is also introduced with considering the cardinality of the discernment frame. Then the weight for each evidence will be generated by the proposed divergence measure method and the modified information volume. Some numerical examples are illustrated to show the outstanding performance of the proposed divergence measure method, and the optimal weighted evidence combination algorithm also gives the relatively highest belief in multi-sensor target recognition.
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This research was funded by National Key Research and Development Program of China, Grant Number 2016YFB0501805.
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Zhao, K., Sun, R., Li, L. et al. An optimal evidential data fusion algorithm based on the new divergence measure of basic probability assignment. Soft Comput 25, 11449–11457 (2021). https://doi.org/10.1007/s00500-021-06040-5
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DOI: https://doi.org/10.1007/s00500-021-06040-5