Uncertainty Measurement for Ultrasonic Sensor Fusion Using Generalized Aggregated Uncertainty Measure 1

Document Type : Research Article

Authors

Department of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran

Abstract

In this paper, target differentiation based on pattern of data which are obtained by a set of two ultrasonic sensors is considered. A neural network based target classifier is applied to these data to categorize the data of each sensor. Then the results are fused together by Dempster–Shafer theory (DST) and Dezert–Smarandache theory (DSmT) to make final decision. The Generalized Aggregated Uncertainty measure named GAU1, as an extension to the Aggregated Uncertainty (AU) is used to evaluate DSmT. Then the GAU1 and AU as the uncertainty measures are applied to the obtained results of the decision makers to evaluate DSmT and DST accordingly. The introduced configuration for decision making has enough flexibility and robustness to use as a distributed sensor network.

Keywords

References

[1]H. Hu, J.Q. Gan, Sensors and data fusion algorithms in mobile robotics University of Essex, Colchester CO4 3SQ, United Kingdom, 10 January 2005.
[2]P. Pong, S. Challa, Empirical analysis of generalized uncertainty measures with Dempster-Shafer fusion, in: 10th Int. Conf. on Information Fusion, Quebec, Que, 9-12 July, 2007.
[3]B. Barshan, B. Ayrulu, S.W. Utete, Neural network-based target differentiation using sonar for robotics applications, IEEE Transactions on Robotics and Automation, 16(4) (2000) 435-442.
[4]K.J. Kyriakopoulos, A. Curran, Ultrasonic navigation for a wheeled non-holonomic vehicle, Journal of Intelligent and Robotic Systems, 12(3) (1995) 239-258.
[5]G. Oriolo, G. Ulivi, M. Vendittelli, Real-time map building and navigation for autonomous robots in unknown environments, IEEE Transactions on Systems, Man and Cybernetics-Part B: (Cybernetics), 28(3) (1998) 316-333.
[6]S. Watanabe, M. Yoneyama, An ultrasonic visual sensor for three-dimensional object recognition using neural networks, IEEE Transactions on Robotics and Automation, 8(2) (1992) 240-249.
[7]M.H. Horng, Performance evaluation of multiple classifications of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers, Expert Systems with Applications, 37 (2010) 4146-4155.
[8]Y. Han, H. Hahn, Localization and classification of target surfaces using two pairs of ultrasonic sensors, Robotics and Autonomous Systems, 33 (2000) 31-41.
[9]X. Li, X. Huang, M. Wang, Robot map building from sonar sensors and DSmT, Information & Security (I&S), 20 (2006) 104-121.
[10]B. Ayrulu, B. Barshan, Reliability measure assignment to sonar for robust target differentiation, Pattern Recognition, 35 (2002) 1403-1419.
[11]Y. Han, M. Han, H. Cha, M. Hong, H. Hahn, Tracking of a moving object using ultrasonic sensors based on a virtual ultrasonic image, Robotics and Autonomous Systems, 36 (2009) 11-19.
[12]M. Khodabandeh, M. Analoui, A. Mohammad- Shahri, Target differentiation using sonar data for robot applications; neural network approach, in: International Conference on Control, Automation and Systems, COEX, Seoul, KOREA, October 17-20, 2007, pp. 1958-1961.
[13]G. Shafer, A mathematical theory of evidence, Princeton Univ. Press, Princeton, NJ, 1976.
[14]A.L. Jousselme, C. Liu, D. Grenier, E. Bosse, IEEE Transaction on Systems, Man and Cybernetics-Part A: Systems and Humans, 36(5) (2006) 890-903.
[15]J.P. Yang, H.Z. Huang, Y. Liu, Y.F. Li, Quantification classification algorithm of multiple sources of evidence, International Journal of Information Technology & Decision Making, 14(5) (2015) 1017-1034.
[16]J. Dezert, J.M. Tacnet, M. Batton-Hubert, F. Smarandache, Multi-criteria decision making based on DSmT-AHP, in: Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2015.
[17]N. Abbas, Y. Chibani, A. Martin, F. Smarandache, The effective use of the DSmT for multi-class classification in: Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2015, pp. 359.
[18]F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 4, American Research Press, 2015.
[19] J. Dezert, Foundation for a new theory of plausible and paradoxical reasoning, Information Security Journal, 9 (2002) 13-57.
[20] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 1, American Research Press, 2004.
[21] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 2, American Research Press, 2006.
[22] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 3, American Research Press, 2009.
[23] J. Dezert, A. Tchamova, F. Smarandache, P. Konstantinova, Target type tracking with PCR5 and Dempster’s rules: a comparative analysis, in: 9th International Conference on Information Fusion, Florence, Italy, 10-13 July 2006.
[24] X. Huang, X. Li, M. Wang, J. Dezert, A fusion machine based on DSmT and PCR5 for robot’s map reconstruction, International Journal of Information Acquisition, 3(3) (2006) 201-211.
[25] R.V.L. Hartley, Transmission of information, Bell Systems & Technology Journal, 7(535-563) (1928).
[26] C.E. Shannon, A mathematical theory of communication, Bell Systems & Technology Journal, 27 (1948) 379-423 and 623-656.
[27] G.J. Klir, M.J. Wierman, Uncertainty-based information 2nd ed. series, Studies in Fuzziness and Soft Computing 15, Physica–Verlag, Heidelberg, Germany, 1999.
[28] G.J. Klir, B. Yuan, Fuzzy sets and fuzzy logic: theory and applications, Prentice–Hall, Upper Saddle River, NJ, 1995.
[29] R.R. Yager, Entropy and specificity in a mathematical theory of evidence, International Journal of General Systems, 9(4) (1983) 249-260.
[30] G.J. Klir, A. Ramer, Uncertainty in Dempster–Shafer theory: a critical re-examination, International Journal of General Systems, 18(2) (1990) 155-166.
[31] T. George, N.R. Pal, Quantification of conflict in Dempster–Shafer framework: a new approach, International Journal of General Systems, 24(4) (1996) 407-423.
[32] N.R. Pal, J.C. Bezdek, R. Hemasinha, Uncertainty measures for evidential reasoning I: a review, International Journal of Approximate Reasoning, 7(3/4) (1992) 165-183.
[33] N.R. Pal, J.C. Bezdek, R. Hemasinha, Uncertainty measures for evidential reasoning II: a new measure of total uncertainty, International Journal of Approximate Reasoning, 8(1) (1993) 1-16.
[34] J.A. Herencia, M.T. Lamata, A generalization of entropy using Dempster–Shafer theory, International Journal of General Systems, 29(5) (2000) 719-735.
[35] A. Ramer, J. Hiller, Total uncertainty revisited, International Journal of General Systems, 26(3) (1997) 223-237.
[36] Y. Maeda, H.T. Nguyen, H. Ichihashi, Maximum entropy algorithms for uncertainty measures, International Journal of Uncertainty, Fuzziness & Knowledge Based Systems, 1(1) (1993) 69-93.
[37] D. Harmanec, G.J. Klir, Measuring total uncertainty in Dempster–Shafer theory, International Journal of General Systems, 22(4) (1994) 405-419.
[38] D. Harmanec, Uncertainty in Dempster–Shafer theory, State University of New York, New York, NY, 1996.
[39] A. Bronevich, G.J. Klir, Measures of uncertainty for imprecise probabilities: an axiomatic approach, International Journal of Approximate Reasoning, 51 (2010) 365-390.
[40] G.J. Klir, H.W. Lewis, Remarks on measuring ambiguity in the evidence theory, IEEE Transaction on Systems, Man and Cybernetics-Part A: Systems and Humans, 38(4) (2008) 995-999.
[41] M. Vatsa, R. Singh, A. Noore, M.M. Houck, Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory, International Journal of Approximate Reasoning, 50 (2008) 51-61.
[42] M. Vatsa, R. Singh, A. Noore, Unification of evidence-theoretic fusion algorithms; a case study in level-2 and level-3 fingerprint features, IEEE Transaction on Systems, Man and Cybernetics-Part A: Systems and Humans, 39(1) (2009) 47-56.
[43] M. Khodabandeh, A. Mohammad-Shahri, Two generalizations of aggregated uncertainty measure for evaluation of Dezert-Smarandache theory, International Journal Information Technology & Decision Making (IJITDM), 11(1) (2012) 119-142.
[44] M. Khodabandeh, A. Mohammad-Shahri, Uncertainty evaluation for an ultrasonic data fusion based target differentiation problem using Generalized Aggregated Uncertainty measure 2, Measurement, 59 (2015) 139-144.
[45] B. Ayrulu, B. Barshan, Identification of target primitives with multiple decision-making sonars using evidential reasoning, International Journal of Robotic Research, 17(6) (1998) 598-623.
AUT Journal of Modeling and Simulation
Volume 49, Issue 1
June 2017
Pages 85-94

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