A Multi-sensor Target Recognition Information Fusion Approach Based on Improved Evidence Reasoning Rule

  • Xiaohan ZhangEmail author
  • Libo Yao
  • Xiaohui Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 972)


The Evidence Reasoning (ER) rule extends traditional Dempster-Shafer evidence theory by establishing a new rule to combine multiple pieces of independent evidence with importance and reliability weights. The importance and reliability weight of an evidence source is usually decided by fusion system designers which is subjective. Aiming at solving the evaluation problem of evidence importance and reliability weight in ER rule, a new method is proposed in this paper under the application background of multi-sensor marine target recognition information fusion. The importance weight of evidence source is calculated based on the accuracy of sensor recognition in history observation, while the reliability weight is calculated based on the improved normalized angle distance which measures the conflicting among pieces of evidence. Then the pieces of weighted evidence are combined under ER rule to draw recognition fusion conclusion. The proposed approach improves the ER rule by giving an objective method to measure the importance and reliability weight of evidence. Simulation experiments are conducted, demonstrating that this approach can combine conflicting evidence more effectively. Moreover, compared with other methods, the improved ER rule shares good convergence performance and has higher computational efficiency, which is beneficial for engineering implementation.


Evidence theory Evidence Reasoning rule Target recognition Information fusion Evidence weight 


  1. 1.
    He, Y., Wang, G., Guan, X.: Information Fusion Theory with Application, 3rd edn. Electronic Industry Press, Beijing (2016)Google Scholar
  2. 2.
    Dempster, A.P.: Upper and lower probabilities induced by a multi-valued mapping. Ann. Math. Stat. 38, 325–339 (1967)zbMATHGoogle Scholar
  3. 3.
    Han, D., Yang, Y., Han, C.: Advances in D-S evidence theory and related discussions. Control Decis. 29(1), 1–11 (2014)zbMATHGoogle Scholar
  4. 4.
    Yang, J.-B., Xu, D.-L.: Evidential reasoning rule for evidence combination. Artif. Intell. 205, 1–29 (2013)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)Google Scholar
  6. 6.
    Zadeh, L.: A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magaz. 7(2), 85–90 (1986)Google Scholar
  7. 7.
    Li, J., Cheng, Y., Liang, Y.: Research of D-ST algorithm based on local conflict distribution strategy. Control Decis. 25(10), 1485–1488 (2010)Google Scholar
  8. 8.
    Deng, Y., Wang, D., Li, Q.: A new method to analyze evidence conflict. Control Theory Appl. 28(6), 839–844 (2011)Google Scholar
  9. 9.
    Yager, R.R.: On the D-S framework and new combination rules. Inf. Sci. 41(2), 93–138 (1987)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Sun, Q., Ye, X., Gu, W.: A new combination rules of evidence theory. Acta Electronica Sinica 28(8), 117–119 (2000)Google Scholar
  11. 11.
    Murphy, C.K.: Combining belief functions when evidence conflicts. Decis. Supp. Syst. 29(1), 1–9 (2000)Google Scholar
  12. 12.
    Guan, X., Yi, X., Sun, X.: Efficient fusion approach for conflicting evidence. J. Tsinghua Univ. (Sci. Tech.) 49(1), 138–141 (2009)Google Scholar
  13. 13.
    Lin, Y., Wang, C., Ma, C.: A new combination method for multisensor conflict information. J. Supercomput. 72, 2874–2890 (2016)Google Scholar
  14. 14.
    Deng, Y., et al.: Combining belief functions based on distance of evidence. Decis. Support Syst. 38(3), 489–493 (2004)MathSciNetGoogle Scholar
  15. 15.
    Song, Y.-f., Wang, X.-d., Lei, L.: Measurement of evidence conflict based on correlation coefficient. J. Commun. 35(5), 95–100 (2014)Google Scholar
  16. 16.
    Wang, L., Mao, Q.-h., Mao, Y.-f.: Weighted evidence combination based on degree of credibility and certainty. J. Commun. 38(1), 83–88 (2017)Google Scholar
  17. 17.
    Li, W., Guo, K.: Combination rules of D-S evidence theory and conflict problem. Syst. Eng. Theory Pract. 8(30), 1422–1432 (2010)Google Scholar
  18. 18.
    Yang, J.B., Xu, D.L.: On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 32(3), 289–304 (2002)Google Scholar
  19. 19.
    Ke, X., Ma, L., Li, Z.: Property research and approach modification of evidential reasoning rule. Inf. Control 45(2), 165–170 (2016)Google Scholar
  20. 20.
    Li, X.-D., Wang, Q.-Q., Wang, F.-Y.: A method of conflictive evidence combination based on the Markov chain. Acta Automatica Sinica 41(5), 915–926 (2015)Google Scholar
  21. 21.
    Liu, X., Deng, J.: Improved D-S method based on conflict evidence correction. J. Electron. Meas. Instrum. 31(9), 1499–1506 (2017)Google Scholar
  22. 22.
    Jousselme, A.L., Grenier, D., Bosse, E.: A new distance between two bodies of evidence. Inf. Fusion 2(1), 91–101 (2001)Google Scholar
  23. 23.
    Li, Y., Guo, Y., Yang, Y.: Identification and application of the evidence conflict based on K-L information distance. Syst. Eng. Theory Pract. 34(8), 2071–2077 (2014)Google Scholar
  24. 24.
    Wang, X., Yang, F.: A kind of evidence combination rule in conflict. J. Missile Guidance 27(5), 255–257 (2007)Google Scholar
  25. 25.
    Hao, Z.-w., Wu, Y., Zhang, J.-d.: Aerial target identification based on bp neural networks and improved combination evidence rule. Electron. Opt. Control 21(12), 36–40 (2014)Google Scholar
  26. 26.
    Liu, S.: Research on infrared ship target recognition in the background of air and sea. Master’s thesis of University of Electronic Science and Technology (2011)Google Scholar
  27. 27.
    Yong, S.: Research on ship recognition with image processing. Comput. Digit. Eng. 43(7), 1207–1211 (2015)Google Scholar
  28. 28.
    Han, D.-q., Han, C.-z., Deng, Y.: Weighted combination of conflicting evidence based on evidence variance. Acta Electronica Sinica 39(3), 153–157 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Institute of Information Fusion of Naval Aeronautical UniversityYantaiChina
  2. 2.No. 91039 Navy of PLABeijingChina

Personalised recommendations