北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (5): 907-913.doi: 10.13700/j.bh.1001-5965.2017.0371

• 论文 • 上一篇    下一篇

基于形态学的自动驾驶仪振动信号基线漂移去噪

张景元, 何玉珠   

  1. 北京航空航天大学 仪器科学与光电工程学院, 北京 100083
  • 收稿日期:2017-06-05 出版日期:2018-05-20 发布日期:2018-05-29
  • 通讯作者: 何玉珠 E-mail:heyuzhuhe@buaa.edu.cn
  • 作者简介:张景元,男,硕士研究生。主要研究方向:自动测试技术与故障诊断;何玉珠,男,博士,教授。主要研究方向:测试系统通用性技术、故障诊断、定位技术。

Removing baseline drift in vibration signal of autopilot based on morphology

ZHANG Jingyuan, HE Yuzhu   

  1. School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2017-06-05 Online:2018-05-20 Published:2018-05-29

摘要: 导弹自动驾驶仪在振动测试过程中存在信号基线漂移且污染严重的问题,而传统的时频处理方法难以达到去噪要求,因此基于形态学基本原理提出了一种用于解决振动信号基线漂移的滤波方法。该滤波方法由3级结构组成,前2级结构均是基于形态学基本原理,第3级进行相消与平滑处理,通过相互级联,可以有效抑制基线漂移。此外,通过引入粒子群优化(PSO)算法使得该滤波方法更具适应性。对比实验利用该滤波方法和对比方法对自动驾驶仪实测振动信号与标准ECG信号进行了处理,结果表明:该滤波方法在抑制基线漂移方面要优于小波阈值去噪和传统的形态学去噪。

关键词: 形态学滤波, 振动信号, 基线漂移, 阈值去噪, 粒子群优化(PSO)

Abstract: The baseline drift and heavy pollution for vibration test of missile autopilot are still problems. The requirement of denoising is difficult to be achieved by traditional time-frequency method. In this paper, to filter out the baseline drift noise, a new morphological filtering method based on the basic principle of generalized morphology is proposed. The proposed method is composed of three-level structure:the former two are based on the morphological principle, and the third level is designed for cancellation and smoothing. Thus, baseline drift can be effectively suppressed by cascading. In addition, the proposed method is more adaptive by introducing particle swarm optimization (PSO). In the final experiments, the real signals of autopilot and ECG signals are denoised by the proposed method and reference methods. The experimental results show that the proposed method is better than wavelet denoising and traditional morphological denoising in suppressing baseline drift.

Key words: morphological filtering, vibration signal, baseline drift, threshold denoising, particle swarm optimization (PSO)

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