Skip to main content
SpringerLink
Log in

An improved evidence fusion algorithm in multi-sensor systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Multi-sensor information fusion plays an important role in practical application. Although D-S evidence theory can handle this information fusion task regardless of prior knowledge, counter-intuitive conclusions may arise when dealing with highly conflicting evidence. To address this weakness, an improved algorithm of evidence theory is proposed. First, a new distribution distance measurement method is first proposed to measure the conflict between the evidences, and the credibility degree of the evidences can be obtained. Next, a modified information volume calculation method is also introduced to measure the effect of the evidence itself, and the information volume of the evidences can be generated. Afterwards, the credibility degree of each evidence can be modified based on the information volume to obtain the weight of each evidence. Ultimately, the weights of the evidences will be used to adjust the body of evidence before fusion. A numerical example for engine fault diagnosis exhibits the availability and effectiveness of the proposed method, where the BPA of the true fault is 89.680%. Furthermore, an application for target recognition is given to show the validity of the proposed algorithm, where the BPA of the true target is 98.948%. The experimental results show that the proposed algorithm has the best performance than other methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Wu Y, Patterson A, Santos R D, Vijaykumar N L (2014) Topology preserving mapping for maritime anomaly detection, pp 313–326. https://doi.org/10.1007/978-3-319-09153-2_24

  2. Callen M, Isaqzadeh M, Long J D, Sprenger C (2014) Violence and Risk Preference: Experimental Evidence from Afghanistan. Amer Econ Rev 104(1):123–148. https://doi.org/10.1257/aer.104.1.123

    Article  Google Scholar 

  3. Abdulhafiz W, Khamis A (2014) Bayesian approach with pre- and post-filtering to handle data uncertainty and inconsistency in mobile robot local positioning. J Intell Syst 23. https://doi.org/10.1515/jisys-2013-0078

  4. Meng W, Hong B-R, Han X-D (2003) Lunar robot information fusion based on D-S evidence theory. J Harbin Instit Technol 35:1040–2

    Google Scholar 

  5. Koch W (2013) Tracking and sensor data fusion: Methodological framework and selected applications, vol 8

  6. Filipowicz W (2014) Mathematical Theory of Evidence in Navigation. In: Cuzzolin F (ed) Belief Functions: Theory and Applications. Third International Conference, BELIEF 2014. Proceedings: LNCS 8764. Belief Functions: Theory and Applications. Third International Conference, BELIEF 2014, Oxford. Springer International Publishing, Cham, p 199–208

  7. Jin Z, Wang X, Gui Q, Liu B, Song S (2013) Improving diagnostic accuracy using multiparameter patient monitoring based on data fusion in the cloud. Lect Notes Electr Eng 276. https://doi.org/10.1007/978-3-642-40861-8_66

  8. Fortino G, Galzarano S, Gravina R, Li W (2014) A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Inf Fusion 22. https://doi.org/10.1016/j.inffus.2014.03.005

  9. Yang D, Zhenghai W, Lin X, Tiankui Z (2014) Online bayesian data fusion in environment monitoring sensor networks. Int J Distrib Sens Netw 2014:1–10. https://doi.org/10.1155/2014/945894

    Google Scholar 

  10. Vinodini Ramesh M (2018) Wireless sensor network for disaster monitoring

  11. Dubois D, Prade H (2001) Possibility theory, probability theory and multiple-valued logics: A clarification. Ann Math Artif Intell 32(1-4):35–66. https://doi.org/10.1023/A:1016740830286

    Article  MathSciNet  Google Scholar 

  12. Broemeling L D (2011) An account of early statistical inference in arab cryptology. Amer Stat 65(4):255–257. https://doi.org/10.1198/tas.2011.10191

    Article  MathSciNet  Google Scholar 

  13. Klaua D (1967) Ein ansatz zur mehrwertigen mengenlehre. Math Nachrichten 33(5-6):273–296. https://doi.org/10.1002/mana.19670330503

    Article  MathSciNet  Google Scholar 

  14. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356. https://doi.org/10.1007/BF01001956

    Article  Google Scholar 

  15. Dempster A P (1967) Upper and lower probabilities induced by a multivalued mapping, pp 57–72. https://doi.org/10.1007/978-3-540-44792-4_3

  16. Shafer G (1976) A mathematical theory of evidence, vol 42

  17. Murphy C K (2000) Combining belief functions when evidence conflicts. Decis Support Syst 29 (1):1–9. https://doi.org/10.1016/S0167-9236(99)00084-6

    Article  Google Scholar 

  18. Yong D, WenKang S, ZhenFu Z, Qi L (2004) Combining belief functions based on distance of evidence. Decis Support Syst 38(3):489–493. https://doi.org/10.1016/j.dss.2004.04.015

    Article  Google Scholar 

  19. Zhang Z, Liu T, Chen D, Zhang W (2014) Novel algorithm for identifying and fusing conflicting data in wireless sensor networks. Sens (Basel, Switzerland) 14:9562–9581. https://doi.org/10.3390/s140609562

    Article  Google Scholar 

  20. Yuan K, Xiao F, Fei L, Kang B, Deng Y (2016) Modeling sensor reliability in fault diagnosis based on evidence theory. Sensors 16:113. https://doi.org/10.3390/s16010113

    Article  Google Scholar 

  21. Xiao F (2019) Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Inf Fusion 46:23–32. https://doi.org/10.1016/j.inffus.2018.04.003

    Article  Google Scholar 

  22. Lin J (1991) Divergence measures based on the shannon entropy. IEEE Trans Inf Theory 37 (1):145–151. https://doi.org/10.1109/18.61115

    Article  MathSciNet  Google Scholar 

  23. Cox R T (1946) Probability, frequency, and reasonable expectation. Am J Phys 14(2):1–13. https://doi.org/10.2307/2272983

    Article  MathSciNet  Google Scholar 

  24. Lamberti P W, Majtey A P (2003) Non-logarithmic jensen-shannon divergence. Physica A: Stat Mech Appl 329(1):81–90. https://doi.org/10.1016/S0378-4371(03)00566-1

    Article  Google Scholar 

  25. Kullback S, Leibler R A (1951) On information and sufficiency. Ann Math Statist 22(1):79–86. https://doi.org/10.1214/aoms/1177729694

    Article  MathSciNet  Google Scholar 

  26. Kullback S (1962) Information theory and statistics. Popul (French Ed) 17:377. https://doi.org/10.2307/1527125

    Google Scholar 

  27. Deng Y (2016) Deng entropy. Chaos Solitons Fractals 91:549–553. https://doi.org/10.1016/j.chaos.2016.07.014

    Article  Google Scholar 

  28. Cui H, Liu Q, Zhang J, Kang B (2019) An improved deng entropy and its application in pattern recognition. IEEE Access 7:18284–18292. https://doi.org/10.1109/ACCESS.2019.2896286.

    Article  Google Scholar 

  29. Fan F, Zuo M (2006) Fault diagnosis of machines based on d-s evidence theory. part 1: D-s evidence theory and its improvement. Pattern Recogn Lett 27:366–376. https://doi.org/10.1016/j.patrec.2005.08.025

    Article  Google Scholar 

  30. Yager R R (1987) On the dempster-shafer framework and new combination rules. Inf Sci 41 (2):93–137. https://doi.org/10.1016/0020-0255(87)90007-7

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This research was funded by National Key Research and Development Program of China, grant number 2016YFB0501805.

Author information

Authors and Affiliations

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

    Kaiyi Zhao, Li Li, Manman Hou, Gang Yuan & Ruizhi Sun

  2. Along.AI Technologies Beijing Ltd, Beijing, China

    Rutai Sun

  3. Scientific research base for Integrated Technologies of Precision Agriculture (animal husbandry), The Ministry of Agriculture, Beijing, 100083, China

    Ruizhi Sun

Authors
  1. Kaiyi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rutai Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manman Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ruizhi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruizhi Sun.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, K., Sun, R., Li, L. et al. An improved evidence fusion algorithm in multi-sensor systems. Appl Intell 51, 7614–7624 (2021). https://doi.org/10.1007/s10489-021-02279-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02279-5

Keywords

Search

Navigation