Heterogeneous information fusion recognition method based on belief rule structure

doi:10.23919/JSEE.2023.000169

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (4): 955-964.doi: 10.23919/JSEE.2023.000169

• SYSTEMS ENGINEERING • Previous Articles

Heterogeneous information fusion recognition method based on belief rule structure

Haibin WANG¹(), Xin GUAN¹(), Xiao YI¹^,*(), Guidong SUN²()

¹ Naval Aviation University, Yantai 264001, China
² Unit 32801 of the PLA, Beijing 100082, China

Received:2022-03-14 Online:2024-08-18 Published:2024-08-06
Contact: Xiao YI E-mail:hesonwhb@163.com;gxtongwin@163.com;yxgx_gxyx@163.com;sdwhsgd@163.com
About author:
WANG Haibin was born in 1982. He received his B.E. degree in electronic information engineering from Harbin Engineering University, Harbin, China, in 2005 and M.E. degree in electronic science and technology from National University of Defense Technology, Changsha, China, in 2007. His research interests are multi-source information fusion and uncertain information processing. E-mail: hesonwhb@163.com

GUAN Xin was born in 1978. She received her B.E. degree in communication engineering from Liaoning University, Liaoning, China, in 1999, and M.E. and Ph.D. degrees in information and communication engineering from Naval Aeronautical Engineering Institute, Yantai, China, in 2002 and 2006, respectively. She is currently a professor at the Naval Avation University. Her research interests are intelligent information processing and multi-source information fusion. E-mail: gxtongwin@163.com
YI Xiao was born in 1976. He received his B.E. degree from Shandong University, Ji’nan, China, in 1998 and M.E., Ph.D. degrees from Naval Aeronautical Engineering Institute, Yantai, China, in 2001, 2005, respectively. He is currently a professor in the Naval Avation University. His research interests are intelligent information processing and multi-source information fusion. E-mail: yxgx_gxyx@163.com

SUN Guidong was born in 1989. He received his B.E. and M.E. degrees from Naval Aeronautical Engineering Institute, Yantai, China, in 2012 and 2015, respectively, and Ph.D. degree from Naval Avation University, Yantai, China, in 2018. His research interests include complex electromagnetic environment, information fusion and intelligent decision making. E-mail: sdwhsgd@163.com
Supported by:
This work was supported by the Youth Foundation of National Science Foundation of China (62001503), and the Special Fund for Taishan Scholar Project (ts 201712072).

Abstract

Abstract:

To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion, but the expert knowledge is not fully utilized, a heterogeneous information fusion recognition method based on belief rule structure is proposed. By defining the continuous probabilistic hesitation fuzzy linguistic term sets (CPHFLTS) and establishing CPHFLTS distance measure, the belief rule base of the relationship between feature space and category space is constructed through information integration, and the evidence reasoning of the input samples is carried out. The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition. Compared with the other methods, the proposed method has a higher correct recognition rate under different noise levels.

Key words: belief rule, heterogeneous information, intention recognition, hesitation fuzzy linguistic

Haibin WANG, Xin GUAN, Xiao YI, Guidong SUN. Heterogeneous information fusion recognition method based on belief rule structure[J]. Journal of Systems Engineering and Electronics, 2024, 35(4): 955-964.

Figures/Tables 5

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References 38

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CPHFLTS original value	Value of CPHFLTS after normalization	CPHFLTS credibility
{[s_0.5,s₁]\|(0.2)},{[s_1.2,s_1.8]\|(0.2)}, {[s_2.1,s_2.8]\|(0.5)}	{[s_0.5,s₁]\|(0.2)},{[s_1.2,s_1.8]\|(0.2)}, {[s_2.1,s_2.8]\|(0.5)},{[s₋₃,s₃]\|(0.1)}	0.5749
{[s_−1.8,s₋₁]\|(0.2)},{[s_−0.8,s_0.9]\|(0.7)}, {[s_1.1,s_1.5]\|(0.1)}	{[s_−1.8,s₋₁]\|(0.2)},{[s_−0.8,s_0.9]\|(0.7)}, {[s_1.1,s_1.5]\|(0.1)}	0.7738
{[s_−1.4,s_−1.2]\|(0.1)},{[s_0.4,s_1.1]\|(0.3)}, {[s_0.8,s_1.7]\|(0.4)}	{[s_−1.4,s_−1.2]\|(0.1)},{[s_0.4,s_1.1]\|(0.3)}, {[s_0.8,s_1.7]\|(0.4)},{[s₋₃,s₃]\|(0.2)}	0.5815
{[s_−2.6,s_−1.5]\|(0.3)},{[s_0.5,s_0.7]\|(0.5)}, {[s_2.4,s_2.6]\|(0.1)}	{[s_−2.6,s_−1.5]\|(0.3)},{[s_0.5,s_0.7]\|(0.5)}, {[s_2.4,s_2.6]\|(0.1)},{[s₋₃,s₃]\|(0.1)}	0.5313

Input sample	Class ω₁ belief	Class ω₂ belief	Class ω₃ belief	Recognition result
y₁	0.5449	0	0.0864	1
y₂	0.3284	0.1691	0.4267	3
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
y₁₀	0	0	0.2961	3
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $
y₁₈	0.2299	0.2938	0	2

Heterogeneous information fusion recognition method based on belief rule structure

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