摘要
针对传统自相关和频谱周期估计方法常会出现虚假周期,提出一种基于非参数检验经验模态分解的周期估计方法。首先对空间翻滚目标的RCS序列进行形态学闭运算提取趋势特征,然后对闭运算后的RCS序列进行经验模态分解,对分解得到的每个本征模态函数(IMF)进行谱分析得到其IMF周期,最后基于非参数检验理论,对每个IMF周期进行分组求秩检验得到目标的翻滚周期。仿真和实测试验结果表明,该方法不仅能够克服虚假周期的影响,而且能够明显改善对翻滚周期的估计精度。
The tradition methods for estimating rolling period based on self-correlation and spectrum analysis often gain false period.In allusion to this problem,a new method based on the nonparametric rank variance test and empirical mode decomposition is proposed.First,the trend character of RCS sequence is extracted by the mathematical morphological closed operation.Then,the extracted RCS sequence is operated by the empirical mode decomposition.And,the periods of each intrinsic mode functions（IMF） are gained by using the spectrum analysis Finally,the rolling period can be estimated by using the nonparametric statistics theory.The experimental results for the emulational and measured data show that the proposed method can solve the false period problem effectively and improve the performance of the estimation.
出处
《宇航学报》
EI
CAS
CSCD
北大核心
2014年第3期356-361,共6页
Journal of Astronautics
基金
国家自然科学基金(61179010)
关键词
翻滚周期
形态学闭
经验模态分解
非参数检验
Rolling period
Mathematical morphological close
Empirical mode decomposition
Nonparametric test
