An active noise control algorithm based on fractional lower order covariance with on-line characteristics estimation

• Published in: Mechanical systems and signal processing Vol. 186; p. 109835
• Main Authors: Feng, Pengxing, Zhang, Lijun, Meng, Dejian, Pi, Xiongfei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023
• Subjects: Active noise control; Fractional lower order covariance; Least mean p-norm; α-stable distribution; Fractional lower order covariance; Least mean p-norm; α-stable distribution; Active noise control
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2022.109835

Active impulsive noise control (AINC) is a specific research interest for improving the performance of traditional algorithms against impulsive environments. By studying existing algorithms, this paper employs a fractional lower order covariance criterion and proposes a filtered-x fractional lower order covariance (FxFLOC) algorithm. After that, the FxFLOC algorithm is equipped with an on-line estimation method using a sampled characteristic function to overcome the dependency on prior knowledge of α-stable distribution. Furthermore, the convergence condition and the influence of hyperparameters are discussed. Extensive simulations are carried out to suggest that the proposed algorithms enhance the stability and accelerate the convergence speed compared with prior algorithms. Also, experiments are conducted to verify the performance of the proposed algorithms. The results show that the proposed algorithms are practical on symmetric α-stable (SαS) impulsive noise attenuation. •An AINC algorithm is proposed using symmetric fractional lower order covariance.•An on-line estimation is applied using a sampled characteristic function method.•Extensive simulations and experiments are carried out to verify the performance.