摘要
在航天发射任务中,子级残骸的跟踪识别的正确性直接关系到任务安全,而传统的均值、方差等雷达散射截面积(RCS)特征未考虑序列时序特性,常导致子级残骸、整流罩等目标识别错误。针对此问题,提出一种频谱和自相关的RCS联合特征识别方法,引入累积频谱均值和累积自相关均值2种新的RCS特征,评估不同特征可分性,并以3种特征优化组合的方式对6次航天发射任务数据集进行训练和测试。试验结果表明:该方法能够有效促进同类目标的聚类效果,获得较好的分类识别结果。该方法可应用于多级火箭分离目标的分类识别场景中,具有一定的工程推广价值。
In aerospace launch missions,the accuracy of tracking and identifying sub-stage debris is directly related to mission safety.Traditional radar cross section(RCS)features such as mean and variance often lead to misidentification of targets like sub-stage debris and fairings,owing to the neglect of sequence temporal characteristics.To address this issue,in this paper,a joint feature recognition method for spectrum and autocorrelation RCS is proposed,in which two novel RCS features,i.e.,cumulative spectrum mean and cumulative autocorrelation mean,are introduced.The separability of different features is evaluated,and an optimized combination of three features is used to train and test datasets from six aerospace launch missions.The experimental results demonstrate that the proposed method effectively enhances the clustering performance of similar targets and achieves favorable classification outcomes.The proposed approach can be applied to classification and recognition scenarios for multi-stage rocket separation targets,demonstrating practical engineering application value.
作者
杨玖文
吴海超
于志坚
YANG Jiuwen;WU Haichao;YU Zhijian(Taiyuan satellite launch center,Taiyuan 030037,Shanxi,China)
出处
《上海航天(中英文)》
2026年第1期54-62,共9页
Aerospace Shanghai(Chinese&English)
关键词
目标识别
雷达散射截面积(RCS)联合特征
滑窗法
累积频谱均值
累积自相关均值
target recognition
joint feature for radar cross section(RCS)
sliding window
cumulative spectrum average
cumulative autocorrelation average
