摘要
火炮发射过程中,内弹道运动参数对射击精度具有重要影响,因此准确分析弹丸的内弹道运动参数具有现实意义。利用弹载存储器,通过获取了火炮发射过程中内弹道加速度信号,构建“弹丸-存储器”两个自由度的力学模型,结合集合经验模态分解方法,开展了非平稳加速度信号分析、自适应分解与重构的研究。在分离出内弹道过程中混叠的特征信号的基础上,对加速度信号进行了强迫响应和自由响应分量的重构,并据此解算了内弹道运动参数。结果表明,集合经验模态分解方法适用于非平稳信号的分析,对重构信号进行解算,得到弹丸实际位移与理论位移相对误差优于1.2%,显著提升了信号分析的准确性和特征识别精度。
The internal ballistic motion parameters have been shown to significantly influence the shooting accuracy during the process of artillery firing.Therefore,the accurate analysis of the internal ballistic motion parameters of projectile holders is of practical significance.An on-board memory was utilised to acquire acceleration signals of internal ballistics during the artillery firing process.A mechanical model with two degrees of freedom for the“projectile-memory”system was constructed,and the Ensemble Empirical Mode Decomposition(EEMD)method was applied to analyse non-stationary acceleration signals,enabling adaptive decomposition and reconstruction.The separation of overlapping characteristic signals during the internal ballistic process enabled the reconstruction of the forced response and free response components of the acceleration signals.The calculation of the internal ballistic motion parameters was then possible.The findings demonstrate the efficacy of the EEMD method in analysing non-stationary signals.The solution of the reconstructed signal has been shown to yield a relative error between the actual and theoretical projectile displacement that is better than 1.2%,thus significantly improving the accuracy of signal analysis and feature recognition.
作者
任星桥
石云波
赵锐
由成欣
闫晓朋
REN Xingqiao;SHI Yunbo;ZHAO Rui;YOU Chengxin;YAN Xiaopeng(Science and Technology on Electronic Test and Measurement Laboratory,North University of China,Taiyuan 030051,Shanxi,China;Shanxi North Machinery Manufacturing Co.,Ltd.,Taiyuan 030000,Shanxi,China)
出处
《火炮发射与控制学报》
北大核心
2025年第6期31-37,46,共8页
Journal of Gun Launch & Control
基金
国家自然科学基金(52175524)。
关键词
内弹道
加速度
集合经验模态分解
信号分析
特征识别
internal ballistics
acceleration
ensemble empirical mode decomposition
signal analysis
feature recognition
