Performance Analysis of Information Fusion Method based on Bell Function

doi:10.23940/ijpe.18.04.p16.729740

Abstract

Abstract:

Multi-node and multi-feature fusion is an important approach for digital modulations signal recognition in modern communication field. Information obtained from multi-node and multi-feature needs to be fused because of incompleteness of single feature and uncertainty of single node. As a powerful method for data fusion and conclusion reasoning in uncertain environments, evidence theory is widely used. Establishing reliable BPA is the prerequisite for evidence fusion. In this paper, in order to improve the coincidence of basic probability assignment (BPA) with real probability, the notion of bell function (Bell-F) based similarity evaluation model (SEM) is introduced. Through comparative experiments, it is proved that the new method based on Bell-F is effective for BPA acquisition. Furthermore, a new information fused based digital signal modulation recognition scheme is described. Finally, a case study is given to illustrate the performance of the proposed model. Through test and calculations under the digital modulation signal data set, under , the recognition rate based on the Bell-F fusion method is above 90%, which is 20% higher than methods without fusion. Under or less, the integrated recognition rate of the Bell-F is 40% higher than the Gray relation method.

Submitted on January 4, 2018; Revised on February 17, 2018; Accepted on March 26, 2018
References: 25

Meiyu Wang, Zhigang Li, Dongmei Huang, and Xinghao Guo. Performance Analysis of Information Fusion Method based on Bell Function [J]. Int J Performability Eng, 2018, 14(4): 729-740.

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	J. X. Cui, B Li, and Y. C. Li, “Generation method and application of basic probability assignment based on normal cloud model[J],” Journal of Data Acquisition and Processing,2015,30(06):1318-1324. (in Chinese)
	A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” The annals of mathematical statistics (1967): 325-339
	Y. Deng, W. Jiang, and X. Xu, et al, “Determining BPA under uncertainty environments and its application in data fusion [J],” Journal of Electronics (China), 2009, 26(1):13-17
	Y. Deng, W. K. Shi, and Z. F. Zhu, et al, “Combining belief functions based on distance of evidence[J],” Decision Support Systems, 2004, 38(3):489 -493
	P. W. Gong, Y. Q. Fei, and L. B. Song, “Traffic-recognition method of hybrid mobile robot based on multi-sensor information fusion[J],” Journal of Shanghai Jiao Tong University,2017,51(04):398-402. (in Chinese)
	Y.B. Gu, and M.L. Yang, “Research on Ofdm and the Single Carrier Signal Modulation Recognition,” Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015 Fifth International Conference on. IEEE, 2015:1245-1249
	D. H. Han, et al, “New dissimilarity measures in evidence theory[C],” Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on, IEEE, 2011: 1-7
	C. Z. Han, D. Q. Han, and J. Jie, “From biological cognition and perception to methodologies of system engineering [J],” Systems Engineering-Theory &Practice, 2008 (supplement):75 -93. (in Chinese)
	B. Y. Kang, Y. Li, Y. Deng, Y. J. Zhang, and X. Y. Deng, “Basic probability assignment generation method based on interval number and its application[J],” Acta Electronica Sinica,2012,40(06):1092-1096. (in Chinese)
	H. L. P. Kumar, and S. Lakshmi, “Automatic Digital Modulation Recognition System Using Feature Extraction,” Emerging Trends in Electrical, Communications and Information Technologies, Springer, Singapore, 201-208, 2017
	Y. Lin, C. Wang, J. X. Wang, and Z. Dou, “A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks[J],” Sensors, 2016, 16(10): 1-22
	Y. Lin, X. Zhu, and Z. Zheng, et al, “The individual identification method of wireless device based on dimensionality reduction and machine learning[J],” The Journal of Supercomputing, 2017: 1-18
	Y. L. Liu, M. Martin, and M. Kraak, “Semantic similarity evaluation model in categorical database generalization,” International Archives of Photogrammetry Remote Sensing And Spatial Information Sciences, 2002, 34(4): 279-285
	M. Liu, B. Z. Yuan., and X. F. Tang, “A new approach to determine the similarity parameters in evidence-theoretic k-NN rule[J],” Acta Electronica Sinica, 2005, 33(4):766 -768. (in Chinese)
	H. M. Mo, X. Lu, and Y. Deng, “A generalized evidence distance[J],” Journal of Systems Engineering and Electronics,2016,27(02):470-476
	G. Shafer, “A mathematical theory of evidence,” Vol. 42. Princeton university press, 1976
	J. Sun, W. Wang, and L. Kou, et al, “A data authentication scheme for UAV ad hoc network communication[J],” The Journal of Supercomputing, 2017: 1-16
	H. Wang, L. Guo, and Z. Dou, et al, “A New Method of Cognitive Signal Recognition Based on Hybrid Information Entropy and DS Evidence Theory[J],” Mobile Networks and Applications, 2018: 1-9
	H. Wang, J. Li, and L. Guo, et al, “Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals[J],” Fractals, 2017, 25(04): 1740008
	C. L. Wen, Z. Zhou, and X. B. XU, “A new similarity measure between generalized trapezoidal fuzzy numbers and its application to fault diagnosis[J],” Acta Electronica Sinica, 2011, 39(3A):1 -6. (in Chinese)
	D. Q. Han, “Multiple classifiers fusion based on weighted evidence combination[A],” IEEE International Conference on Automation and Logistics [C]. Jinan, China: IEEE, 2007.2138 - 2143. (in Chinese)
	C. L. Wen, D. H Zhou, Q. Pan., and H. C. Zhang, “Distributed information fusion algorithm for single sensor dynamic system on the basis of multiscale dynamic models [J],” Acta Automatica Sinica, 2001, 27(2):158-165. (in Chinese)
	C. L. Wen, Z. Zhou, and X. B. XU, “A new similarity measure between generalized trapezoidal fuzzy numbers and its application to fault diagnosis[J],” Acta Electronica Sinica, 2011, 39(3A):1 -6. (in Chinese)
	L. A. Zadeh, “A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination,” AI magazine 1986, 7(2): 85
	W. H. Zhang, J. S. Yuan, S. Wang, and L. Zhang, “A Transformer Failure Basic Reliability Allocation Calculation Method Based on Improved Three-ratio Method[J],” Power System Protection and Control,2015,43(07):115-121. (in Chinese)