基于经验模态分解的外弹道降噪方法及应用

Noising Reduction Algorithm Based on Empirical Mode Decomposition (EMD) and Applications in Calculating Trajectories of Spacecraft

针对外弹道测量数据信号的非平稳性,引入经验模态分解法,根据工程实际应用需求进行改进,提出实用的基于经验模态分解的外弹道降噪方法。新算法验证了外弹道噪声的分布规律并反映了经验模态分解算法的特性。相对于多项式滤波算法,新算法保证了数据信息完整性,有效地降低了噪声的不利影响,提高数据信息的可靠性和真实性。通过仿真和工程实践计算,验证了该算法的有效性和适用性。

Noising Reduction is an important technology in the field of spaceflight. Generally the signal of Trajectories is non - stationary. This paper advances new Empirical Mode Decomposition （EMD） noising reduction algorithm to meet data processing need. New method validates the law of Trajectories noise distributing, and shows the characteristic of EMD. Relative to polynomial filter, the method ensures integrated data information, and reduces bad influence on Trajectories availably. Through resembling using the data from project practice, new idea is valid and applicable.