Robust PBFD Nonlinear Filtering Scheme for Active Noise Cancellation With Remote Acoustic Sensing

Nonlinear active noise cancellation/control (NANC) systems have attracted attention for attenuating noises using remote acoustic sensing techniques. Controlling noises at the remote virtual loaction involve higher computational requirements. In this paper, a novel robust partitioned-block frequency-domain (PBFD) nonlinear filtering scheme is proposed by combining the half-quadratic criterion (HQC) and the fast Fourier transform (FFT) method, which is called the partitioned-block frequency-domain functional link network based on HQC (PBFDFLN-HQC) algorithm. The PBFD approach for the filtering and adaptation operations can reduce the latency and computational load. A PBFDFLN-HQC-based remote virtual NANC frame has also been developed to form the partitioned-block frequency-domain filtered-s HQC (PBFDFsHQC) algorithm. Moreover, the convergence property and complexity of the proposed algorithm are analyzed. Numerical experiments verify the superiority of the proposed algorithm with computational efficiency and robustness to impulsive interferences. Realistically attenuating noises are carried out to exhibit controlling effects of the proposed algorithm for factory noises in remote virtual NANC systems.