Skip to main content
SpringerLink
Log in

Self-adaptive combination method for temporal evidence based on negotiation strategy

  • Research Paper
  • Special Focus on Multi-source Information Fusion
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

In temporal information fusion, the information collected by sensors is obtained dynamically with the passage of time. Unlike the spatial information fusion, temporal fusion should be dynamic. Evidence theory has been applied to fuse temporal and spatial information; however, existing temporal fusion methods do not treat conflicting and non-conflicting evidence sources distinctively. Moreover, unlike spatial evidence sources, which are obtained simultaneously, temporal evidence sources cannot be evaluated simultaneously to obtain their degree of reliability. Thus, it is necessary to develop a method for temporal evidence combination. In this paper, a self-adaptive combination method for temporal evidence is proposed based on the negotiation strategy. In the proposed method, a set called an evidence set is constructed by the cumulative temporal fusion results of the previous moment, current moment, and future moment. The evidence set is evaluated as conflicting or non-conflicting according to the maximum power pignistic probability distance between each pair of evidence sources in the set. Then, temporal evidence sources are self-adaptively combined by different methods according to the degree of conflict. Numerical experiments were conducted to evaluate the performance of the proposed method. The results indicated that the proposed method is sufficiently effective and robust to support decision making.

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

Similar content being viewed by others

References

  1. Song Y F, Fu Q, Wang Y F, et al. Divergence-based cross entropy and uncertainty measures of Atanassov’s intuitionistic fuzzy sets with their application in decision making. Appl Soft Comput, 2019, 84: 105703

    Article  Google Scholar 

  2. Zhang B, Zhang M L, Song Y F, et al. Combining evidence sources in time domain with decision maker’s preference on time sequence. IEEE Access, 2019, 7: 174210

    Article  Google Scholar 

  3. Song Y F, Wang X D, Quan W, et al. A new approach to construct similarity measure for intuitionistic fuzzy sets. Soft Comput, 2019, 23: 1985–1998

    Article  Google Scholar 

  4. Song Y F, Wang X D, Zhu J W, et al. Sensor dynamic reliability evaluation based on evidence theory and intuitionistic fuzzy sets. Appl Intell, 2018, 48: 3950–3962

    Article  Google Scholar 

  5. Song Y F, Wang X D, Lei L, et al. A novel similarity measure on intuitionistic fuzzy sets with its applications. Appl Intell, 2015, 42: 252–261

    Article  Google Scholar 

  6. Lei L, Song Y F, Luo X. A new re-encoding ECOC using reject option. Appl Intell, 2020, 50: 3090–3100

    Article  Google Scholar 

  7. Dempster A P. Upper and lower probabilities induced by a multivalued mapping. Ann Math Statist, 1967, 38: 325–339

    Article  MathSciNet  Google Scholar 

  8. Shafer G. A Mathematical Theory of Evidence. Princeton: Princeton University Press, 1976

    Book  Google Scholar 

  9. Fei L G, Lu J D, Feng Y Q. An extended best-worst multi-criteria decision-making method by belief functions and its applications in hospital service evaluation. Comput Industrial Eng, 2020, 142: 106355

    Article  Google Scholar 

  10. Liu Z G, Pan Q, Dezert J, et al. Combination of classifiers with optimal weight based on evidential reasoning. IEEE Trans Fuzzy Syst, 2018, 26: 1217–1230

    Article  Google Scholar 

  11. Fei L G, Feng Y Q, Liu L N. Evidence combination using OWA-based soft likelihood functions. Int J Intell Syst, 2019, 34: 2269–2290

    Article  Google Scholar 

  12. Liu Z G, Pan Q, Dezert J, et al. Classifier fusion with contextual reliability evaluation. IEEE Trans Cybern, 2018, 48: 1605–1618

    Article  Google Scholar 

  13. Fei L G, Feng Y Q. A novel retrieval strategy for case-based reasoning based on attitudinal Choquet integral. Eng Appl Artif Intell, 2020, 94: 103791

    Article  Google Scholar 

  14. Liu Z G, Pan Q, Dezert J, et al. Hybrid classification system for uncertain data. IEEE Trans Syst Man Cybern Syst, 2017, 47: 2783–2790

    Article  Google Scholar 

  15. Fei L G, Feng Y Q, Liu L N. On pythagorean fuzzy decision making using soft likelihood functions. Int J Intell Syst, 2019, 34: 3317–3335

    Article  Google Scholar 

  16. Liu Z G, Liu Y, Dezert J, et al. Evidence combination based on credal belief redistribution for pattern classification. IEEE Trans Fuzzy Syst, 2020, 28: 618–631

    Article  Google Scholar 

  17. Fei L G, Feng Y Q. An attitudinal nonlinear integral and applications in decision making. Int J Fuzzy Syst, 2020, 24: 1–9

    Google Scholar 

  18. Liu Z G, Zhang X X, Niu J W, et al. Combination of classifiers with different frames of discernment based on belief functions. IEEE Trans Fuzzy Syst, 2020. doi: https://doi.org/10.1109/TFUZZ.2020.2985332

  19. Wu D D, Tang Y C. An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory. Qual Reliab Engng Int, 2020, 36: 1786–1807

    Article  Google Scholar 

  20. Hong L, Lynch A. Recursive temporal-spatial information fusion with applications to target identification. IEEE Trans Aerosp Electron Syst, 1993, 29: 435–445

    Article  Google Scholar 

  21. Song Y F, Wang X D, Lei L. Combination of temporal evidence sources based on intuitionistic fuzzy sets (in Chinese). Acta Autom Sin, 2016, 42: 1322–1338

    MATH  Google Scholar 

  22. Chen Y F, Xa X Z, Hu D G, et al. Evidence combination based on tentative discount of evidences (in Chinese). Acta Electron Sin, 2014, 42: 756–765

    Google Scholar 

  23. Luo H, Yin Y P, Hu X X, et al. Method to modify evidence source based on trustworthy factors (in Chinese). Syst Eng Electron, 2015, 37: 1459–1464

    MATH  Google Scholar 

  24. Wen C L, Wang Y C, Xu X B. Fuzzy information fusion algorithm of fault diagnosis based on similarity measure of evidence. In: Proceedings of the 5th International Symposium on Neural Networks. Berlin: Springer, 2008. 5264: 506–515

    Google Scholar 

  25. Yu C, Yang J H, Yang D B, et al. An improved conflicting evidence combination approach based on a new supporting probability distance. Expert Syst Appl, 2015, 42: 5139–5149

    Article  Google Scholar 

  26. Bi W H, Zhang A, Li C. Weighted evidence combination method based on new evidence conflict measurement approach (in Chinese). Control Dec, 2016, 31: 73–78

    Google Scholar 

  27. Hu C H, Si X S, Zhou Z J, et al. An improved D-S algorithm under the new measure criteria of evidence conflict (in Chinese). Acta Electron Sin, 2009, 37: 1578–1583

    Google Scholar 

  28. Liu Y X, Zhu Y P, Li X, et al. Integrated target discrimination model in missile defense system (in Chinese). J Electron Inform Technol, 2006, 28: 638–642

    Google Scholar 

  29. Wu J, Cheng Y M, Pan Q, et al. An effective multi-platform multi-radar target identification algorithm based on three level fusion hierarchical structure (in Chinese). J Northwestern Polytech Univ, 2012, 30: 367–372

    Google Scholar 

  30. Xu X B, Zhang Z, Li S B, et al. Fault diagnosis based on fusion and updating of diagnosis evidence (in Chinese). Acta Autom Sin, 2016, 42: 107–121

    Google Scholar 

  31. Liu W R. Analyzing the degree of conflict among belief functions. Artif Intell, 2006, 170: 909–924

    Article  MathSciNet  Google Scholar 

  32. Jousselme A L, Maupin P. Distances in evidence theory: comprehensive survey and generalizations. Int J Approx Reason, 2012, 53: 118–145

    Article  MathSciNet  Google Scholar 

  33. Jousselme A L, Grenier D, Bossé R. A new distance between two bodies of evidence. Inf Fusion, 2001, 2: 91–101

    Article  Google Scholar 

  34. Tessem B. Approximations for efficient computation in the theory of evidence. Artif Intell, 1993, 61: 315–329

    Article  MathSciNet  Google Scholar 

  35. Zhu J W, Wang X D, Song Y F. A new distance between BPAs based on the power-set-distribution pignistic probability function. Appl Intell, 2018, 48: 1506–1518

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61703426, 61876189, 61806219), China Post-Doctoral Science Foundation (Grant No. 2018M633680), Young Talent Fund of University Association for Science and Technology in Shaanxi, China (Grant No. 20190108), and Innovation Talent Supporting Project of Shaanxi, China (Grant No. 2020KJXX-065).

Author information

Authors and Affiliations

  1. College of Air and Missile Defense, Air Force Engineering University, Xi’an, 710051, China

    Yafei Song & Xiaodan Wang

  2. Institute of High Technology, Air Force Engineering University, Xi’an, 710051, China

    Jingwei Zhu

  3. College of Information and Navigation, Air Force Engineering University, Xi’an, 710051, China

    Lei Lei

Authors
  1. Yafei Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingwei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaodan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yafei Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, Y., Zhu, J., Lei, L. et al. Self-adaptive combination method for temporal evidence based on negotiation strategy. Sci. China Inf. Sci. 63, 210204 (2020). https://doi.org/10.1007/s11432-020-3045-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-3045-5

Keywords

Search

Navigation