SELF-OPTIMIZING REAL-VALUED GENERALIZED ADAPTIVE NOTCH FILTER

Generalized adaptive notch filters (GANFs) are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. The tracking properties of a GANF algorithm depend on two adaptation gains, which should be chosen so as to match the degree of nonstationarity of the identified system. First, an analytical study of a tracking performance of a real-valued GANF algorithm is presented. Then, based on the obtained theoretical results, a self-optimizing algorithm is proposed, capable of automatic tuning of its adaptation gains. The paper is an extension of the previous work devoted to complex-valued GANF algorithms.