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Sensor Data Fusion Based on a New Conflict Measure

Author

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  • Wen Jiang
  • Boya Wei
  • Xiyun Qin
  • Jun Zhan
  • Yongchuan Tang

Abstract

Dempster-Shafer (D-S) evidence theory has been widely used in various fields. However, how to measure the degree of conflict (similarity) between the bodies of evidence is an open issue. In this paper, in order to solve this problem, firstly we propose a modified cosine similarity to measure the similarity between vectors. Then a new similarity measure of basic probability assignment (BPAs) is proposed based on the modified cosine similarity. The new similarity measure can achieve the reasonable measure of the similarity of BPAs and then efficiently measure the degree of conflict among bodies of evidence. Numerical examples are used to illustrate the effectiveness of the proposed method. Finally, a weighted average method based on the new BPAs similarity is proposed, and an example is used to show the validity of the proposed method.

Suggested Citation

  • Wen Jiang & Boya Wei & Xiyun Qin & Jun Zhan & Yongchuan Tang, 2016. "Sensor Data Fusion Based on a New Conflict Measure," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-11, August.
    • Handle: RePEc:hin:jnlmpe:5769061
    DOI: 10.1155/2016/5769061
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    Cited by:

    1. Bowen Qin & Fuyuan Xiao, 2019. "An improved method to determine basic probability assignment with interval number and its application in classification," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.

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