Multichannel block adaptive filters for real‐time, nonparametric identification of time‐varying plants

Many real‐world applications of active noise control are characterized by transfer functions that vary significantly and unpredictably. The controller’s transfer function models must adapt to these variations. Presented here is a class of adaptive filters that accomplish quasiperiodic system identification updates for feedforward control by using blocks of input–output histories. The algorithms form a one‐dimensional family linking normalized LMS adaptive filters and optimal Wiener filters, and are termed ‘‘block projection’’ algorithms. The system identification proceeds noninvasively, producing nonparametric (FIR) impulse responses. The multichannel generalization and application of these algorithms to system identification, as presented here, is novel. Considerations are described that arise from the algorithms’ implementation in the context of system identification; in particular, the proper weighting of input and output data pairs is discussed. The resulting multichannel control algorithms have been implemented successfully for quieting of a compact distributed source in an anechoic environment, and for local quieting of a diffuse field in a reverberant room. In both cases, error microphones could be moved about, providing a ‘‘mobile quiet zone,’’ and performance was obtained for bandwidths exceeding a decade.