A modified filtered‐x least mean square/fourth algorithm using weighted reference signal for active noise control. (30th January 2022)

• Record Type: Journal Article
• Authors: Zhou, Zhengdao; Chen, Shuming; Jiang, Yao; Meng, Hao
• Is Part Of: International journal of adaptive control and signal processing. Volume 36:Number 4(2022)
• Page Start: 882
• Page End: 900
• Publication Date: 2022-01-30
• Subjects: active noise control -- adaptive algorithm -- convex combination -- weighted reference signal; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3375
• Languages: English
• ISSNs: 0890-6327

Abstract

Abstract: Filtered‐x least mean square (FxLMS) algorithm and its variants have been prevalently applied in the field of active noise control. However, these algorithms still suffer from relatively low convergence rates and steady‐state noise reduction. In this article, we propose a new idea to improve the performance of the conventional filtered‐x least mean square/fourth (FxLMS/F) algorithm by weighting reference signal samples, thus a weighted reference signal FxLMS/F (WRS‐FxLMS/F) algorithm is presented. Besides, to solve the contradiction between convergence speed and steady‐state noise reduction, the convex combination method is combined with the WRS‐FxLMS/F algorithm, and a convex combination WRS‐FxLMS/F (CWRS‐FxLMS/F) algorithm is developed. The computational complexity of the proposed algorithms is analyzed, and the simulation results demonstrate that the proposed algorithms have better performance both in the steady‐state noise reduction and convergence rate than the traditional FxLMS, NFxLMS, and FxLMS/F algorithm.