期刊文献+

基于多尺度分析的传感器信号去噪 被引量:1

Signal De-noising Method based on Multi-scale Analysis
下载PDF
导出
摘要 环境建模技术是机器人自主导航研究中的一个关键问题。针对机器人自主环境建模过程中噪声信息处理问题,提出一种基于小波变换多尺度分析的改进阈值噪声滤除算法,并比较不同小波函数的去噪效果,实现准确环境建模。通过分析传感器信号的特性,利用小波变换的多尺度分析特性,运用改进阈值算法来抑制包含有噪声的传感器信息在不同尺度上的噪声小波系数,实现在重构信号中去噪的目的。通过Pioneer3-DX机器人平台验证了所提出的方法能有效的滤除噪声,提高环境建模的准确性。 Environment modeling is one of the most important issues to tackle in autonomous navigation. Dealing with signal de-nois-ing problems in the environment modeling of autonomous mobile robot,a signal de-noising method based on multi-scale analysis of wavelet transform by improved threshold function was proposed,and to compare the difference of the effects by different wavelet types. It realized the accurate environment modeling. Analyzed the character of sensor signal and using a character of multiscale analysis of wavelet transform analysis,the aim of reconstructed signal de-noising was realized by using a method that wavelet trans-form coefficients of noise-sensor signal were filtered by changing threshold on the different scale.Experiment result on Pioneer3-DX proves that the proposed method filters the noise-sensor signal efficiently,and improves the environment modeling precision efficiently.
作者 徐立 李磊民
出处 《微计算机信息》 2009年第34期85-87,共3页 Control & Automation
基金 国防科工委基础项目 基金申请人:李磊民 (项目名称 基金编号不公开)
关键词 噪声滤除 传感器信号 环境建模 多尺度分析 小波变换 de-noising sensor signal environment modeling multi-scale analysis wavelet transform
  • 相关文献

参考文献6

二级参考文献33

  • 1王楠,杜劲松.小波消噪在振动信号处理中的应用[J].仪器仪表学报,2001,22(z1):225-226. 被引量:13
  • 2王鹏飞,李著信,范文峰.基于改进型小波基的图像压缩方法研究[J].微计算机信息,2005,21(10X):97-99. 被引量:10
  • 3王俊,陈逢时,张守宏.一种利用子波变换多尺度分辨特性的信号消噪技术[J].信号处理,1996,12(2):105-109. 被引量:48
  • 4崔锦泰 程正兴(译).小波分析导论[M].西安:西安交通大学出版社,1995..
  • 5[1]Y Uny Cao, Alex S, Fukunaga, et al. Cooperative Mobile Robotics: Antecedents and Directions Autonomous Robots. 1997, (4): 7~27
  • 6[2]Clark F, Olson. Probabilistic Self-Localization for Mobile Robots. IEEE Transactions on Robotics and Automation, 2000, 16(1): 55~66
  • 7[3]Brian Yamauchi. Frontier-Based Exploration Using Multiple Robots. In: Proceedings of the Second International Conference on Autonomous Agents(Agents'98), Minneapolis, MN, 1998. 47~53
  • 8[4]S. Thrun, M Beetz, et al. Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva. The International Journal of Robotics Research, 2000, 19(11): 972~999
  • 9[5]Masahiro Tomono, Shin' ichi Yuta. A Framework for Indoor Navagation based on a Partially Quantitative Map. In: Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, Japan, 2000, 1: 626~632
  • 10[6]J Borenstein, H R Everett, L Feng, et al. Mobile Robot Positioning: Sensors and Techniques. Journal of Robotic Systems, 1997, 14: 231~249

共引文献78

同被引文献5

  • 1梁靓,黄玉清.基于嵌入式PC的机器人光电寻线系统[J].西南科技大学学报,2004,19(4):1-3. 被引量:4
  • 2梁靓,黄玉清,张玲霞,李想.机器人的差分方向控制与实现[J].信息与电子工程,2004,2(3):196-199. 被引量:4
  • 3Thrun S,Bayesian Landmark Learning for Mobile Robot Localization[J].Machine Learning,1998,33(1):41-76.
  • 4Kenneth D.Harries,Michael Recce.Absolute localization for a mobile robot using place cells[J].Robotics and Autonomous Systems.1997,22(3):393-406.
  • 5Greg Welch,Gary Bishop.An Introduction to the Kalman Filter.[EB/OL].http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf.,2007.1.16.

引证文献1

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部