Influencing factor analysis of interception probability and classification-regression neural network based estimation

doi:10.23919/JSEE.2023.000092

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (4): 992-1006.doi: 10.23919/JSEE.2023.000092

• CONTROL THEORY AND APPLICATION • Previous Articles

Influencing factor analysis of interception probability and classification-regression neural network based estimation

Yi NAN¹(), Guoxing YI¹^,*(), Lei HU¹(), Changhong WANG¹(), Zhenbiao TU²()

¹ Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
² Beijing Electro-mechanical Engineering Institute, Beijing 100074, China

Received:2021-06-30 Online:2023-08-18 Published:2023-08-28
Contact: Guoxing YI E-mail:nanyi11@163.com;ygx@hit.edu.cn;maple_hsjz@163.com;cwang@hit.edu.cn;tuzhenbiao@vip.sina.com
About author:
NAN Yi was born in 1989. She received her M.S. degree in control science and engineering, School of Astronautics, Harbin Institute of Technology in 2014. She is pursuing her Ph.D. degree in Harbin Institute of Technology. Her research fields include reinforcement learning, weapon system combat effectiveness evaluation, machine learning, and decision making. E-mail: nanyi11@163.com

YI Guoxing was born in 1974. He received his Ph.D. degree in control science and engineering, School of Astronautics, Harbin Institute of Technology. His research fields include unmanned aerial vehicle (UAV) system and application technology, the mechanism and application of hemispherical resonance gyro, and the research of inertial and integrated navigation. E-mail: ygx@hit.edu.cn

HU Lei was born in 1993. He received his M.S. degree in control science and engineering, School of Astronautics, Harbin Institute of Technology in 2018. He is pursuing his Ph.D. degree in Harbin Institute of Technology. His research fields include artificial intelligence, unmanned aerial vehicle (UAV) cluster, weapon system combat effectiveness, and decision-making. E-mail: maple_hsjz@163.com

WANG Changhong was born in 1961. He received his B.E., M. E., and Ph.D. degrees from Harbin Institute of Technology, Harbin, China in 1983, 1986 and 1991, respectively. He is presently a full professor and the Deputy Dean of Academy of Science and Technology, Harbin Institute of Technology. His research interests include intelligent control and intelligent system, inertial technology, robotics, and precision servo system. E-mail: cwang@hit.edu.cn

TU Zhenbiao was born in 1977. He received his B.S. degree from Wuhan University of Science and Engineering, Wuhan, China, in 1999, and M.S. degree from Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently a researcher with Beijing Electro-Mechanical Engineering Institute, Beijing, China. His current research interests include operational effectiveness and mission planning. E-mail: tuzhenbiao@vip.sina.com
Supported by:
This work was supported by the Foundation Strengthening Program Technology Field Foundation (2020-JCJQ-JJ-132)

Abstract

Abstract:

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation, while its influencing factors are complex and mutually coupled. Existing calculation methods have very limited analysis of the influence mechanism of influencing factors, and none of them has analyzed the influence of the guidance law. This paper considers the influencing factors of both the interceptor and the target more comprehensively. Interceptor parameters include speed, guidance law, guidance error, fuze error, and fragment killing ability, while target performance includes speed, maneuverability, and vulnerability. In this paper, an interception model is established, Monte Carlo simulation is carried out, and the influence mechanism of each factor is analyzed based on the model and simulation results. Finally, this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors. The proposed method reduces the interference of invalid interception data to valid data, so its prediction accuracy is significantly better than that of pure regression neural networks.

Key words: interception probability, simulation modeling, analysis of influencing factors, probability estimation, neural networks

Yi NAN, Guoxing YI, Lei HU, Changhong WANG, Zhenbiao TU. Influencing factor analysis of interception probability and classification-regression neural network based estimation[J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 992-1006.

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Influencing factor	Parameter
Guidance law	SMGAAC or PG
Velocity of the interceptor	${V_m}$
Guidance error	${\sigma _G}$
Fuze delay time error	${\sigma _D}$
Fragment	${N_{{\rm{FT}}} }$ and ${\sigma _F}$
Velocity of the target	${V_t}$
Maneuver	Spiral ( ${R_{{\rm{IT}}} }$ , ${V_m}$ ) or no maneuver
Vulnerability	${h_T}$ and ${S_{{\rm{TV}}} }$

${P_{{\rm{area}}} }$	NN	MAE	${ {\rm{AE} }_{{\rm{max}}} }$
${P_{{\rm{area}}} }$	NN	MAE	AE	ACT	EST
0.01−1.0	CR	0.025	0.259	0.414	0.156
0.01−1.0	PR	0.037	0.552	0.689	0.137
0.1−0.3	CR	0.026	0.179	0.291	0.112
0.1−0.3	PR	0.041	0.246	0.255	0.009
0.3−0.5	CR	0.053	0.259	0.414	0.156
0.3−0.5	PR	0.075	0.362	0.414	0.052
0.5−0.7	CR	0.027	0.058	0.689	0.631
0.5−0.7	PR	0.123	0.552	0.689	0.138
0.7−0.9	CR	0.021	0.021	0.720	0.699
0.7−0.9	PR	0.058	0.058	0.720	0.662

${P_{{\rm{area}}} }$	NN	MAPE	${ {\rm{APE} }_{{\rm{max}}} }$
${P_{{\rm{area}}} }$	NN	MAPE	APE	ACT	EST
0.01−0.9	CR	0.145	0.707	0.101	0.172
0.01−0.9	PR	0.226	0.963	0.255	0.009
0.1−0.3	CR	0.153	0.707	0.101	0.172
0.1−0.3	PR	0.041	0.246	0.255	0.009
0.3−0.5	CR	0.053	0.259	0.414	0.156
0.3−0.5	PR	0.075	0.362	0.414	0.052
0.5−0.7	CR	0.027	0.058	0.689	0.631
0.5−0.7	PR	0.123	0.552	0.689	0.138
0.7~ 0.9	CR	0.021	0.021	0.720	0.699
0.7~ 0.9	PR	0.058	0.058	0.720	0.662

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Influencing factor analysis of interception probability and classification-regression neural network based estimation

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