应用分层自适应匹配追踪重构MEMS陀螺信号

杨金显; 杨闯

doi:10.3788/OPE.20172512.3160

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应用分层自适应匹配追踪重构MEMS陀螺信号

信息科学 | 更新时间：2020-07-07

- 应用分层自适应匹配追踪重构MEMS陀螺信号
- MEMS gyro signal reconstruction via layerwise and adaptive MP
- 光学精密工程 2017年25卷第12期页码：3160-3168
- 作者机构：
  
  河南理工大学电气工程与自动化学院导航制导实验室, 河南焦作 454000
- 作者简介：
  
  [ "杨金显(1980-), 男, 山东曹县人, 博士, 副教授, 硕士生导师, 2008年于哈尔滨工程大学获得博士学位, 主要从事MEMS惯性测量及在随钻、电网运动和变形监测中的应用研究。E-mail:yangjinxian@hpu.edu.cn" ]
  [ "杨闯(1992-), 男, 河南滑县人, 硕士研究生, 主要从事惯性测量及其应用研究。E-mail:yang_ch126@126.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(41672363);国家自然科学基金资助项目(U1404510);国家自然科学基金资助项目(61440007);河南省科技攻关专项资金资助项目(172102210289);河南省创新型科技人才队伍建设工程资助项目(CXTD2016054);河南省高校基本科研业务费专项资金资助项目(NSFRF1619);河南理工大学杰出青年基金资助项目(J2017-5)
- DOI：10.3788/OPE.20172512.3160
  中图分类号： TN966;TP391.4
- 收稿日期：2017-04-12，
  
  录用日期：2017-6-7，
  
  纸质出版日期：2017-12-25
- 稿件说明：
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杨金显, 杨闯. 应用分层自适应匹配追踪重构MEMS陀螺信号[J]. 光学精密工程, 2017,25(12):3160-3168.

Jin-xian YANG, Chuang YANG. MEMS gyro signal reconstruction via layerwise and adaptive MP[J]. Optics and precision engineering, 2017, 25(12): 3160-3168.
杨金显, 杨闯. 应用分层自适应匹配追踪重构MEMS陀螺信号[J]. 光学精密工程, 2017,25(12):3160-3168. DOI： 10.3788/OPE.20172512.3160.

Jin-xian YANG, Chuang YANG. MEMS gyro signal reconstruction via layerwise and adaptive MP[J]. Optics and precision engineering, 2017, 25(12): 3160-3168. DOI： 10.3788/OPE.20172512.3160.

摘要

对含噪微机械系统（MEMS）陀螺信号进行小波分解重构时，真实信号对应的小波系数很难选取，故本文提出一种分层自适应匹配追踪算法（LAMP）来解决上述问题。建立了含噪MEMS陀螺信号中信号小波系数稀疏提取架构，将信号小波系数提取问题转化为含噪信号中信号小波系数稀疏性的恢复问题。比较已有稀疏重构算法，采用一种新的LAMP算法，在各种可能的小波系数组合中挑选出分解系数最为稀疏的一组，以此消除信号中的噪声小波系数，进而重构MEMS陀螺信号。实验表明：提出的LAMP算法的稀疏重构效果优于其他迭代贪婪重构算法；基于LAMP的信号稀疏小波重构方法，可以有效去除MEMS陀螺信号的大量噪声；去噪前后，纯MEMS陀螺数据解算的方位角平均累积误差由10.060 2（°）/h减小到5.034 6（°）/h，优于传统小波阈值重构法平均累积误差8.596 8（°）/h，显示了较好的应用效果。

Abstract

When noisy signals from a Micro-electromechanical System(MEMS) gyro are reconstructed by wavelet decomposes

it is difficult to choose the wavelet coefficients corresponding to the real signals. Therefore

a kind of Layerwise and Adaptive Matching Pursuit-based(LAMP-based)algorithm was proposed to solve the problem mentioned above. A sparse extraction construction of wavelet coefficients from the real MEMS gyro signal was established by recovering the sparsity of the wavelet coefficients of the noisy gyro signal. Then

the new LAMP algorithm was designed to pick out the most sparse wavelet coefficients among all wavelet coefficients of the noisy MEMS gyro signal

and the chosen wavelet coefficients were utilized to reconstruct the real MEMS gyro signal. Substantial experiment results indicate that the proposed LAMP algorithm is superior to other iterative greedy reconstruction algorithms. It effectively removes substantial MEMS gyro noise

and corresponding azimuth average error reduces from 10.060 2 (°)/h (before LAMP reconstruction) to 5.034 6 (°)/h (after LAMP reconstruction)

which shows its denoising performance to be better than that of the traditional wavelet threshold reconstruction method with an azimuth average error of 8.596 8 (°)/h(after wavelet threshold reconstruction). It concludes that the proposed LAMP-based signal reconstruction method for MEMS gyro has good application prospect.

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