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


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

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

  1. 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;;;;
  • 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:

    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:

    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:

    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:

    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:
  • Supported by:
    This work was supported by the Foundation Strengthening Program Technology Field Foundation (2020-JCJQ-JJ-132)


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