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A New Multi-Sensor Target Recognition Framework based on Dempster-Shafer Evidence Theory

Volume 14, Number 6, June 2018, pp. 1224-1233
DOI: 10.23940/ijpe.18.06.p13.12241233

Kan Wang

Southwest China Institute of Electronic Technology, CETC Intelligent Joint intelligence Key  Laboratory, Chengdu, 610036, China

(Submitted on February 25, 2018; Revised on April 4, 2018; Accepted on May 3, 2018)


In order to meet the higher requirements in military technology, automation, and intelligence, increasingly importance has been attached to the information fusion for multi-sensor systems. Dempster-Shafer evidence theory is a typical method of uncertainty information fusion due to its adjustability in uncertainty modeling; whereas classical evidence theory is still insufficient in solving high-conflict problems. This assumption studies the multi-sensor information fusion model based on evidence theory from the following aspects. First, it introduces the basic principles of evidence theory, and focuses on how to use triangular fuzzy numbers to obtain basic probability assignments. Second, the method of weighting the evidence using the reliability of the sensor is introduced. The reliability of the sensor is divided into two parts: static reliability and dynamic reliability. Moreover, this model proposes the irrationality of Deng's entropy weight for the binary target recognition problem, and improves the entropy weight in sensor dynamic weights. Finally, on the basis of the above research, sensor sensing data is applied to this model. Through simulation experiments, the validity of the model is proved and the target can be accurately identified.


References: 23

        1. B. Cai, Y. Zhao, H. Liu, and M. Xie, “A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems,” IEEE Transactions on Power Electronics, vol. 32, no. 7, pp. 5590-5600, July 2017
        2. B. Chen, J. Wang, and S. Chen, “Prediction of pulsed GTAW penetration status based on BP neural network and DS evidence theory information fusion,” The International Journal of Advanced Manufacturing Technology, vol. 48, no. 1-4, pp. 83-94, April 2010
        3. M. Delgado, J. A. C. Corrales, J. J. Saucedo-Dorantes, R. D. J. Romero-Troncoso and R. A. A. Osornio-Rios, “Thermography based Methodology for Multi-fault Diagnosis on Kinematic Chain[J],” IEEE Transactions on Industrial Informatics, pp. 1-1, March 2018
        4. Y. Deng, “Deng Entropy: A Generalized Shannon Entropy to Measure Uncertainty”, (online since January 2015) (DOI: 10.5281/zenodo.32211)
        5. G. Dong and G. Kuang, “Target Recognition via Information Aggregation Through Dempster–Shafer's Evidence Theory[J],” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 6, pp. 1247-1251, February 2015
        6. D. Grießbach, D. Baumbach, A. Börner, M. Buder, I. Ernst, E. Funk, J. Wohlfeil and S. Zuev, “IPS–A system for real-time navigation and 3D-modeling,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), pp. 21-26, Melbourne, Australia, July 2012
        7. H. Guo, W. Shi and Y. Deng, “Evaluating sensor reliability in classification problems based on evidence theory,” IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, vol. 36, no. 5, pp. 970-981, November 2006
        8. T. Horiuchi, “Decision rule for pattern classification by integrating interval feature values,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 4, pp. 440-448, April 1998
        9. A. L. Jousselme, D. Grenier and É. Bossé, “A new distance between two bodies of evidence,” Information fusion, vol. 2, no. 2, pp. 91-101, June 2001
        10. H. Li, J. Zhao, X. Zhang, and X. Ni, “Fault Diagnosis for Machinery based on Feature Selection and Probabilistic Neural Network,” International Journal of Performability Engineering, vol. 13, no. 7, pp. 1165-1170, November 2017
        11. Y. Lin, C. Wang, C. Ma, Z. Dou, and X. Ma, “A new combination method for multisensor conflict information,” Journal of Supercomputing, vol. 72, no. 7, pp. 2874-2890, March 2016
        12. Y. Lin, X. Zhu, Z. Zheng, Z. Dou, and R. Zhou, “The individual identification method of wireless device based on dimensionality reduction and machine learning,” Journal of Supercomputing, no. 5, pp. 1-18, December 2017
        13. K. Ma, H. Li, H. Yong, Z. Wang, D. Meng and L. Zhang, “Robust multi-exposure image fusion: A structural patch decomposition approach[J],” IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2519-2532, February 2017
        14. C. U. Mba, H. A. Gabbar, S. Marchesiello, A. Fasana, and L. Garibaldi, “Fault Diagnosis in Flywheels: Case Study of a Reaction Wheel Dynamic System with Bearing Imperfections,” International Journal of Performability Engineering, vol. 13, no. 4, pp. 362-373, July 2017
        15. E. Ristani, and C. Tomasi, “Features for Multi-Target Multi-Camera Tracking and Re-Identification,” Computer Vision and Pattern Recognition, Mar 2018
        16. C. Shi, Z. Dou, Y. Lin, and W. Li, “Dynamic threshold-setting for RF-powered cognitive radio networks in non-Gaussian noise,” Physical Communication, vol. 27, pp. 99-105, April 2018
        17. G. Wang, N. Li, and Y. Zhang, “An event based multi-sensor fusion algorithm with deadzone like measurements,” Information Fusion, vol. 42, pp. 111-118, July 2018
        18. L. Yan, Y. Lu, and Y. Zhang, “An improved NLOS identification and mitigation approach for target tracking in wireless sensor networks,” IEEE Access, vol. 5, no. 99, pp. 2798-2807, March 2017
        19. H. Yin, “Tensor Sparse Representation for 3-D Medical Image Fusion Using Weighted Average Rule[J],” IEEE Transactions on Biomedical Engineering, pp. 1-1, February 2018
        20. K. Yuan, F. Xiao, L. Fei, B. Kang, and Y. Deng, “Modeling sensor reliability in fault diagnosis based on evidence theory,” Sensors, vol. 16, no. 1, pp. 113, January 2016
        21. L. A. Zadeh, “Review of a mathematical theory of evidence,” AI magazine, vol. 5, no. 3, pp. 235-247, September 1984
        22. X. Zhang, F. Zhao, and J. Kang, “Case Studies for Bearing Fault Diagnosis based on Adaptive Myriad Filter and Alpha Stable Model,” International Journal of Performability Engineering, vol. 13, no. 4, pp. 551-555, July 2017
        23. W. Zhao, H. Lu and D. Wang, “Multisensor image fusion and enhancement in spectral total variation domain[J],” IEEE Transactions on Multimedia, vol. 20, no. 4, pp. 866-879, October 2018


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