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首页> 外文会议>Proceedings of NOISE-CON 08 >A Kalman Filter Approach to Active Duct Noise Control UsingTI TMS320C6713 DSP
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A Kalman Filter Approach to Active Duct Noise Control UsingTI TMS320C6713 DSP

机译：使用TI TMS320C6713 DSP的主动管道噪声控制的卡尔曼滤波器方法

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In this paper a Kalman filter fitted in the feedback active noise control system is employed forrnsuppressing duct noise. The secondary path of a duct noise system is modeled by an IIR filter,rnwhereas the controller filter is modeled by an FIR filter. The secondary path dynamics isrnobtained by an off-line system identification technique. The controller FIR filter is tuned on-linernby the developed Kalman algorithm. Computer simulations show that Kalman algorithmrnoutperforms the FXLMS algorithm in minimizing the duct noise, especially for multi-tone noisernand white noise. To verify the effectiveness of the Kalman algorithm, experiments are conductedrnfor duct noise suppression. A TI TMS320C6713 DSP is used for implementing the complexrnKalman algorithm. While the Kalman algorithm and the FXLMS algorithm may haverncomparable performance in reducing pure-tone duct noise, noises with two-tone or more (evenrnthe narrowband white noise) can be shown experimentally to be reduced more by using thernKalman algorithm than the FXLMS algorithm. The experiment results indicated that up to 30dBrnand 20dB reductions was measured for two-tone and three-tone noise by using the Kalmanrnalgorithm, respectively, while a 6.8dB reduction is observed for a 200-300 narrowband whiternnoise. Finally, a fan operating at 750Hz is used as a noise source, the Kalman algorithm is shownrnto achieve a 7.5dB noise reduction and only 3 dB noise suppression is obtained by using thernFXLMS algorithm.
机译：本文采用安装在反馈有源噪声控制系统中的卡尔曼滤波器来抑制管道噪声。管道噪声系统的次级路径由IIR滤波器建模，而控制器滤波器由FIR滤波器建模。次级路径动力学通过离线系统识别技术获得。控制器的FIR滤波器通过开发的Kalman算法进行在线调整。计算机仿真表明，卡尔曼算法在最小化风道噪声方面优于FXLMS算法，特别是对于多音调噪声和白噪声而言。为了验证卡尔曼算法的有效性，进行了风管噪声抑制实验。 TI TMS320C6713 DSP用于实现complexrnKalman算法。虽然Kalman算法和FXLMS算法在减少纯音管道噪声方面可能具有不可比的性能，但使用卡尔曼算法可以比FXLMS算法在实验上显示出具有两个或两个以上音调的噪声（窄带白噪声）在实验上得以减少。实验结果表明，使用卡尔曼算法可分别测量两声和三声噪声，分别降低30dBrn和20dB，而在200-300窄带白噪声下可降低6.8dB。最后，将风扇工作在750Hz频率作为噪声源，显示了Kalman算法可实现7.5dB的噪声降低，而使用FXLMS算法仅可抑制3 dB的噪声。

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  • 来源
    《Proceedings of NOISE-CON 08》|2008年|p.1-6|共6页
  • 会议地点 Dearborn MI(US);Dearborn MI(US);Dearborn MI(US)
  • 作者

    J. Shaw; C.L. Huang; S.Y. Lu;

  • 作者单位

    Institute of Mechatronic Engineering National Taipei University of Technology Taipei 106, Taiwan Email: jshaw@ntut.edu.tw;

    rnInstitute of Mechatronic Engineering National Taipei University of Technology Taipei 106, Taiwan Email: t5408011@ntut.edu.tw;

    rnDepartment of Occupational Safety and Health Chung Shan Medical University Taichung City 402, Taiwan Email: sylu@csmu.edu.tw;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 振动、噪声及其控制;
  • 关键词

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