以TI TMS320C6713 DSP實現卡爾曼濾波器於主動式管路減噪

A Kalman Filter Approach to Active Duct Noise Control Using TI TMS320C6713 DSP

黃玠理(Chieh-Li Huang)

指導教授 ： 蕭俊祥

國立臺北科技大學/機電學院/機電整合研究所/碩士(2008年)

https://doi.org/10.6841/NTUT.2008.00493

摘要

本論文應用卡爾曼濾波器於回授式主動管路減噪系統可有效減低噪音。第二路徑的管路噪音系統是採用IIR濾波器以離線系統鑑別得到。控制器則是以FIR濾波器經由卡爾曼演算法來進行線上調整，經由電腦模擬可得知其更能減低管路噪音，特別在複頻噪音部分。為了證實其卡爾曼演算法能有效的應用在管路減噪上，本篇以TI TMS320C6713 DSK去實現複雜的卡爾曼演算法。由實驗可知，卡爾曼演算法與FXLMS演算法在處理單頻噪音擁有相似的減噪效果，但卡爾曼演算法在雙頻、多頻甚至窄頻能分別減下30dB、20dB以上和平均3.3dB的減噪效果，明顯較傳統的FXLMS演算法好。對於鼓風機，卡爾曼演算法更可以有效減少其機具噪音達7.5dB。

關鍵字

卡爾曼濾波器 ； IIR濾波器 ； FIR濾波器 ； FXLMS演算法 ； 回授式主動減噪

並列摘要

In this paper a Kalman filter fitted in the feedback active noise control system is derived for suppressing duct noise. The secondary path of a duct noise system is modeled by an IIR filter, whereas the controller filter is modeled by an FIR filter. The secondary path dynamics is obtained by off-line system identification technique. The controller FIR filter is tuned on-line by the developed Kalman algorithm. Computer simulations show that Kalman algorithm outperforms the FXLMS algorithm in minimizing the duct noise, especially for broadband noise. To verify the effectiveness of the Kalman algorithm, experiments are conducted for duct noise suppression.
A TI TMS320C6713 DSP is used for implementing the complex Kalman algorithm. While the Kalman algorithm and the FXLMS algorithm may have comparable performance is reducing pure-tone duct noise, noises with two-tone or more (even the broadband noise) can be shown experimentally to be reduced more by using the Kalman algorithm than the FXLMS algorithm. The experimental results indicated that up to 30dB and 20dB noise reduction was obtained for two-tone and three-tone noise respectively with the application of Kalman algorithm, while an average 3.3dB noise reduction for the broadband noise was observed. Finally, duct noise originated from a blower is used, the Kalman algorithm is shown to achieve a noise suppression of 7.5dB, better than the FXLMS algorithm does.

並列關鍵字

Kalman filter ； IIR filter ； FIR filter ； FXLMS algorithm ； Feedback active noise control

參考文獻

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