Binormalized data-reusing adaptive filtering algorithm for active control of impulsive sources |
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| Affiliation: | 1. Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, China;2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China;1. Key Laboratory of Optoelectronic Technology & Systems, Education Ministry of China, Chongqing University, 400044 Chongqing, China;2. School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;1. Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu 610031, China;2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;3. Chengdu University of Information Technology, Chengdu 610225, China;4. Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA;5. School of Electrical and Information Engineering, Xi’an Jiaotong University, Xi’an, China |
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| Abstract: | The main objective of active noise control (ANC) is to provide attenuation for the environmental acoustic noise. The adaptive algorithms for ANC systems work well to attenuate the Gaussian noise; however, their performance may degrade for non-Gaussian impulsive noise sources. Recently, we have proposed variants of the most famous ANC algorithm, the filtered-x least mean square (FxLMS) algorithm, where an improved performance has been realized by thresholding the input data or by efficiently normalizing the step-size. In this paper, we propose a modified binormalized data-reusing (BNDR)-based adaptive algorithm for impulsive ANC. The proposed algorithm is derived by minimizing a modified cost function, and is based on reusing the past and present samples of data. The main contribution of the paper is to develop a practical DR-type adaptive algorithm, which incorporates an efficiently normalized step-size, and is well suited for ANC of impulsive noise sources. The computer simulations are carried out to demonstrate the effectiveness of the proposed algorithm. It is shown that an improved performance has been realized with a reasonable increase in the computational complexity. |
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| Keywords: | Active noise control Impulsive noise Binormalized data-reusing Adaptive algorithm |
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