摘要
针对靶场试验中异常弹丸着靶速度无法准确测试的情况,为了提高异常弹丸着靶速度的预测精度,从弹丸着靶前后的雷达瀑布图中提取出RGB图像信息,由弹丸着靶前后的径向速度中挖掘出关键点的数字信息,实现对异常弹着靶速度的预测。首先把图像信息和数字信息等二元信息分别作为特征向量,对应的着靶速度实测值作为目标向量,利用训练数据分别建立支持向量回归机模型,挖掘着靶速度中的非线性特征,把测试数据带入所建模型预测出对应的着靶速度。同时利用训练数据建立GM(1,1)灰色模型,挖掘着靶速度中的线性特征并对着靶速度进行预测。其次将三个模型对训练数据的拟合值构建为特征向量,对应的着靶速度实测值作为目标向量,建立遗传算法优化LSSVM模型。最后将三个模型对测试数据的预测值代入建立好的遗传算法优化LSSVM模型中,得到了该模型预测出的着靶速度。实验结果表明,对比支持向量回归机、多元线性回归和随机森林,遗传算法优化LSSVM预测精度更高,误差远小于1‰,可以作为异常弹着靶速度的预测模型。
Aiming at the problem of predicting the impact velocity of abnormal projectile,RGB image information was extracted from the radar waterfall before and after the projectile impacting,and digital information of key points was extracted from the radial velocity before and after the projectile impacting.Firstly,binary information such as image information and digital information were used as feature vectors,corresponding to the measured impact velocity as the target vector.Based on training data,support vector regression machine models were established to mine nonlinear features of the impact velocity.By introducing test data into the established model,the corresponding impact velocity can be predicted.At the same time,a GM(1,1)grey model was established using training data to mine linear features of the impact velocity,and then the corresponding target velocity of the test data can be predicted.Secondly,the fitting values of the three models to the training data were constructed as feature vectors,corresponding to the measured impact velocity as the target vector,and a genetic algorithm was established to optimize the LSSVM model.Finally,the predicted values of the three models on the test data were substituted into the established genetic algorithm optimized LSSVM model,and the predicted target velocity of the model was obtained.The results show that compared with support vector regression machine,multiple linear regression and random forest,genetic algorithm optimized LSSVM has higher prediction accuracy,and the error is far less than 1‰,which can be used as the prediction model of impact velocity of abnormal projectile.
作者
田珂
TIAN Ke(Unit 63861 of PLA,Baicheng 137001,China)
出处
《弹道学报》
CSCD
北大核心
2023年第2期102-110,共9页
Journal of Ballistics
关键词
着靶速度
图像信息
数字信息
二元信息
遗传算法优化
异常弹
impact velocity
image information
digital information
binary information
genetic algorithm optimization
abnormal projectile
