Journal of Systems Engineering and Electronics ›› 2011, Vol. 33 ›› Issue (8): 1861-1864.doi: 10.3969/j.issn.1001-506X.2011.08.34

• 制导、导航与控制 • 上一篇    下一篇

导引头的EMD-KF组合滤波方法

赵振昊,沈毅,王冬明   

  1. 哈尔滨工业大学航天学院, 黑龙江 哈尔滨 150001
  • 出版日期:2011-08-15 发布日期:2010-01-03

EMD-KF filtering method for seeker

ZHAO Zhen-hao,SHEN Yi,WANG Dong-ming   

  1. School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
  • Online:2011-08-15 Published:2010-01-03

摘要:

在拦截大机动来袭目标时,拦截弹视线角速度呈无规律的非线性变化,导引头对自身测量信息滤波存在一定困难。针对这一背景本文提出一种基于经验模态分解(empirical mode decomposition, EMD)和卡尔曼滤波(Kalman filtering, KF)相结合的导引头滤波降噪方法,发挥经验模态分解处理非平稳信号的自适应特性,并通过卡尔曼估计削弱经验模态分解方法中“边界效应”的影响。仿真表明,该方法对于具有强非线性特性的视线角速度信号有较好的自适应滤波效果。

Abstract:

While intercepting a highly maneuvering target, the interceptor has trouble in filtering the signal measured by the seeker due to the reason that the angular rate of the interceptor’s sight line changes irregularly and nonlinearly. Therefore a novel de-noising approach, the empirical mode decomposition-Kalman filtering (EMDKF) method, is proposed to solve this problem. This method which synthesizes the merits of both EMD and Kalman filtering, can filter the nonlinear signals adaptively while attenuating the end effect of EMD by  applying Kalman estimation. Simulation results indicate that the proposed method has an adaptive filtering performance for the strongly nonlinear angular rate signal of the interceptor’s sight line.