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

作　　者：NAN Yi YI Guoxing HU Lei WANG Changhong TU Zhenbiao 

机构地区：[1]Department of Control Science and Engineering,Harbin Institute of Technology,Harbin 150001,China [2]Beijing Electro-mechanical Engineering Institute,Beijing 100074,China

出　　处：《Journal of Systems Engineering and Electronics》2023年第4期992-1006,共15页系统工程与电子技术（英文版）

基　　金：supported by the Foundation Strengthening Program Technology Field Foundation(2020-JCJQ-JJ-132)。

摘　　要：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.

关 键 词：interception probability simulation modeling analysis of influencing factors probability estimation neural networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TJ765[自动化与计算机技术—控制科学与工程]