上海航天（中英文）2026，Vol.43Issue(1)：54-62,9.DOI:10.19328/j.cnki.2096-8655.2026.01.005
一种频谱和自相关的雷达散射截面积联合特征分类识别方法
A Joint Feature Classification Recognition Method for Spectrum and Autocorrelation RCS
杨玖文 1吴海超 1于志坚1
作者信息
- 1. 太原卫星发射中心,山西 太原 030037
- 折叠
摘要
Abstract
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.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Key words
target recognition/joint feature for radar cross section(RCS)/sliding window/cumulative spectrum average/cumulative autocorrelation average分类
信息技术与安全科学引用本文复制引用
杨玖文,吴海超,于志坚..一种频谱和自相关的雷达散射截面积联合特征分类识别方法[J].上海航天（中英文）,2026,43(1):54-62,9.
