Target threat estimation based on discrete dynamic Bayesian networks with small samples

doi:10.23919/JSEE.2022.000076

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1135-1142.doi: 10.23919/JSEE.2022.000076

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Target threat estimation based on discrete dynamic Bayesian networks with small samples

Fang YE^1,²(), Ying MAO^1,²(), Yibing LI^1,^2,*(), Xinrui LIU^1,²()

¹ College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
² Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China

Received:2021-09-28 Accepted:2022-06-21 Online:2022-10-27 Published:2022-10-27
Contact: Yibing LI E-mail:yefang0923@126.com;maoying666@hrbeu.edu.cn;liyibing@hrbeu.edu.cn;rebeccalxr@foxmail.com
About author:|YE Fang was born in 1980. She received her Ph.D. degree from Harbin Engineering University in 2006. She is currently working in the College of Information and Communication Engineering, Harbin Engineering University. Her research interests include cognitive confrontation and intelligent decision-making. E-mail: yefang0923@126.com||MAO Ying was born in 1998. She is pursuing her M.S. degree in the College of Information and Communication Engineering, Harbin Engineering University. Her research interest is situation awareness. E-mail: maoying666@hrbeu.edu.cn||LI Yibing was born in 1967. He received his Ph.D. degree from Harbin Engineering University in 2003. He is a professor in the College of Information and Communication Engineering, Harbin Engineering University. His research interests include communication signal processing and radio navigation and positioning. E-mail: liyibing@hrbeu.edu.cn||LIU Xinrui was born in 1988. She received her Ph.D. degree from Saint Pertersburg State University of Information Technologies Mechanics and Optics. She is a lecturer in the College of Information and Communication Engineering, Harbin Engineering University. Her research interest is THz communication. E-mail: rebeccalxr@foxmail.com
Supported by:
This work was supported by the Fundamental Scientific Research Business Expenses for Central Universities (3072021CFJ0803), and the Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Key Laboratory Project (AMCIT21V3)

Abstract

Abstract:

The accuracy of target threat estimation has a great impact on command decision-making. The Bayesian network, as an effective way to deal with the problem of uncertainty, can be used to track the change of the target threat level. Unfortunately, the traditional discrete dynamic Bayesian network (DDBN) has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing. Considering the finiteness and discreteness of DDBN parameters, a fuzzy k-nearest neighbor (KNN) algorithm based on correlation of feature quantities (CF-FKNN) is proposed for DDBN parameter learning. Firstly, the correlation between feature quantities is calculated, and then the KNN algorithm with fuzzy weight is introduced to fill the missing data. On this basis, a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning. Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing, and improve the effect of DDBN parameter learning in the case of serious sample missing. With the proposed method, the final target threat assessment results are reasonable, which meets the needs of engineering applications.

Key words: discrete dynamic Bayesian network (DDBN), parameter learning, missing data filling, Bayesian estimation

Fang YE, Ying MAO, Yibing LI, Xinrui LIU. Target threat estimation based on discrete dynamic Bayesian networks with small samples[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1135-1142.

Figures/Tables 8

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Variable	Variable state
TTR	High, middle, low
ST	High, middle, low
RT	High, middle, low
D	Far, moderate, near
V	Far, slow
A	Frontage, flank, rear
PW	Wide, medium, narrow
WF	High, middle, low
RF	High, middle, low
WBSF	Slow, quick, zero

Time slice	Index
Time slice	D	V	A	PW	WF	RF	WBSF
TS1	Far	Slow	Front	Wide	Middle	Low	Slow
TS2	Far	Slow	front	Wide	Middle	Low	Slow
TS3	Middle	Fast	Front	Wide	Middle	Middle	Quick
TS4	Near	Fast	Front	Wide	Middle	High	Quick
TS5	Near	Fast	Front	Wide	Middle	High	Zero

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Target threat estimation based on discrete dynamic Bayesian networks with small samples

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