摘要
针对弹药超高速侵彻多层建筑物的过程中,侵彻过载加速度信号产生粘连混叠,影响侵彻穿层特征的精准辨识提取,造成引信难以精确计层的问题,提出一种基于多点最优最小熵反褶积(MOMEDA)和交互信息的过载信号穿层特征提取方法。考虑到引信加速度敏感系统在高频强动载下的响应规律未知,该方法利用MOMEDA的非迭代盲解卷积增强技术来实现对原始侵彻过载信号的降噪,基于交互信息理论进一步优化MOMEDA最佳滤波器的长度以增强原始侵彻过载信号中多层目标特征。通过对引信超高速侵彻多层靶板的仿真、试验信号的研究结果表明,该方法可以有效突显原始侵彻过载信号中的穿层特征,为强粘连信号下的引信精确计层功能实现提供依据。
In the process of ammunition penetrating into multilayer buildings at high speed,the penetration overload acceleration signal presents the characteristics of adhesion and aliasing,which heavily affects the accurate identification and extraction of penetration features,and makes it difficult for the fuze to count the layers of targets accurately.To solve the above problems,this paper proposed a feature extraction method for penetration overload signal based on multi-point optimal minimum entropy deconvolution adjustment(MOMEDA)and mutual information.Considering that the response law of the fuze acceleration sensitive system was unknown under high-frequency strong dynamic loads,the proposed method utilized the non-iterative blind deconvolution enhancement technology of MOMEDA to achieve noise reduction for the original penetration overload signal.To further enhance the highlighting of multi-layer target features in the original overload signal,the length of the MOMEDA filter was further optimized based on the mutual information theory.Finally,the verification results of the simulation and test signals of the fuze indicated that the proposed method could effectively highlight the penetration characteristics in the original overload acceleration signal,which providing a basis for the accurate layer counting function under strong aliasing signals.
作者
谢雨岑
房安琪
郜王鑫
李彩芳
邵志豪
张珂
唐万杰
XIE Yucen;FANG Anqi;GAO Wangxin;LI Caifang;SHAO Zhihao;ZHANG Ke;TANG Wanjie(Science and Technology on Electromechanical Dynamic Control Laboratory,Xi'an 710065,China;Xi'an Institute of Electromechanical Information Technology,Xi'an 710065,China;Jilin Jiangji Specific Industrial Ltd,Jilin 132000,China)
出处
《探测与控制学报》
CSCD
北大核心
2024年第5期1-7,共7页
Journal of Detection & Control
关键词
超高速侵彻
多点最优最小熵反褶积
交互信息
特征提取
hypervelocity penetration
multi-point optimal minimum entropy deconvolution adjustment
mutual information
feature extraction
