Hybrid data fusion model for restricted information using Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system

  • E. BrumanciaEmail author
  • S. Justin Samuel
  • L. Mary Gladence
  • Karunya Rathan


Information fusion is the crux of data fusion which is used to compare large numerical data using normalization and aggregate function. The recent trends in information fusion have the limitations in fusing the important details in order to overcome the security issues in both low-level and high-level information fusion systems. The information from heterogeneous sources is different from one another like conceptual, contextual and graphical. This information is included for the fusion process, but the ancient approach has the limitation, and this research work proposes a new model of information fusion for the restricted content for various types of information. In this research work, an algorithm for information fusion is implemented for decision making based on Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system. Proposed hybrid work is applicable in data fusion process based on the theoretical approach of Dempster–Shafer and then into ANFIS. The proposed data fusion method is employed in a wide range of fields which include robotics, statistics, estimation and control.


Data fusion Information fusion Heterogeneous information Restricted information 


Compliance with ethical standards

Conflict of interest

No conflict of interest. No animals are used in this work. All materials are our own.


  1. Abdolkarimi ES, Mosavi MR, Abedi AA, Mirzakuchaki S (2015) Optimization of the low-cost INS/GPS navigation system using ANFIS for high speed vehicle application. In: Signal processing and intelligent systems conference (SPIS), pp 93–98Google Scholar
  2. Bassford M, Painter B (2016) Intelligent bio-environments: exploring fuzzy logic approaches to the honeybee crisis. In: International conference on intelligent environments (IE), pp 202–205Google Scholar
  3. Dubois D, Liu W, Ma J, Prade H (2016) The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks. Inf Fusion 32:12–39CrossRefGoogle Scholar
  4. Ehlenbröker J-F, Mönks U, Lohweg V (2016) Sensor defect detection in multisensory Information fusion. J Sens Sens Syst 5:337–353CrossRefGoogle Scholar
  5. Fan C-T, Wang Y-K, Huang C-R (2017) Heterogeneous information fusion and visualization for a large-scale intelligent video surveillance system. IEEE Trans Syst Man Cybern Syst 47(4):593–604CrossRefGoogle Scholar
  6. Ghamisi P, Benediktsson JA, Phinn S (2015) Land-cover classification using both hyperspectral and LiDAR data. Int J Image Data Fusion 6(3):189–215CrossRefGoogle Scholar
  7. Ghamisi P, Höfle B, Zhu XX (2017) Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network. IEEE J Sel Top Appl Earth Observ Remote Sens 10(6):3011–3024CrossRefGoogle Scholar
  8. Jiang L, Yan L, Xia Y, Guo Q, Fu M, Lu K (2017) Asynchronous multirate multisensor data fusion over unreliable measurements with correlated noise. IEEE Trans Aerosp Electron Syst 53(5):2427–2437CrossRefGoogle Scholar
  9. Khodadadzadeh M, Li J, Prasad S, Plaza A (2015) Fusion of hyperspectral and LiDAR remote sensing data using multiple feature learning. IEEE J Sel Top Appl Earth Observ Remote Sens 8(6):2971–2983CrossRefGoogle Scholar
  10. Li H (2016) Research on target information fusion identification algorithm in multi-sky-screen measurement system. IEEE Sens J 16(21):7653–7658CrossRefGoogle Scholar
  11. Li H, Song Y, Philip Chen CL (2017a) Hyperspectral image classification based on multiscale spatial information fusion. IEEE Trans Geosci Remote Sens 55(9):5302–5312CrossRefGoogle Scholar
  12. Li Y, Jha DK, Ray A, Wettergren TA (2017b) Information fusion of passive sensors for detection of moving targets in dynamic environments. IEEE Trans Cybern 47(1):93–104CrossRefGoogle Scholar
  13. Lin Guoping, Liang Jiye, Qian Yuhua (2015) An information fusion approach by combining multigranulation rough sets and evidence theory. Inf Sci 314:184–199MathSciNetCrossRefzbMATHGoogle Scholar
  14. Miao Z, Shi W, Samat A, Lisini G, Gamba P (2016) Information fusion for urban road extraction from vhr optical satellite images. IEEE J Sel Top Appl Earth Observ Remote Sens 9(5):1817–1829CrossRefGoogle Scholar
  15. Mönks U, Trsek H, Dürkop L, Geneib V, Lohweg V (2015) Towards distributed intelligent sensor and information fusion. J Mechatron 34:63–71CrossRefGoogle Scholar
  16. Pichon F, Destercke S, Burger T (2015) A consistency-specificity trade-off to select source behavior in information fusion. IEEE Trans Cybern 45(4):598–609CrossRefGoogle Scholar
  17. Rasti B, Ghamisi P, Plaza J, Plaza A (2017) Fusion of hyper spectral and LiDAR data using sparse and low-rank component analysis. IEEE Trans Geosci Remote Sens 55(11):6354–6365CrossRefGoogle Scholar
  18. Ribeiro RA, Falcao A, Mora A, Fonseca JM (2014) FIF: a fuzzy information fusion algorithm based on multi-criteria decision making. Knowl Based Syst 58:23–32CrossRefGoogle Scholar
  19. Tapia-Rosero A, Bronselaer A, De Mol R, De Tré G (2016) Fusion of preferences from different perspectives in a decision-making context. Inf Fusion 29:120–131CrossRefGoogle Scholar
  20. Yuan J, Chen H, Sun F, Huang Yalou (2015) Multisensor information fusion for people tracking with a mobile robot: a particle filtering approach. IEEE Trans Instrum Meas 64(9):2427–2442CrossRefGoogle Scholar
  21. Zhao W, Xu Z, Zhao J (2016) Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion. Inf Fusion 27:138–149CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • E. Brumancia
    • 1
    Email author
  • S. Justin Samuel
    • 1
  • L. Mary Gladence
    • 1
  • Karunya Rathan
    • 1
  1. 1.School of ComputingSathyabama Institute of Science and TechnologyChennaiIndia

Personalised recommendations