Self-optimizing adaptive notch filters - Comparison of three optimization strategies

The paper provides comparison of three different approaches to on-line tuning of adaptive notch filters (ANF) - the algorithms used for extraction/elimination of complex sinusoidal signals (cisoids) buried in noise. Tuning is needed to adjust adaptation gains, which control tracking performance of ANF algorithms, to the unknown and/or time time-varying rate of signal nonstationarity. The first of the compared approaches incorporates sequential optimization of adaptation gains. The second solution is based on the concept of parallel estimation. It is shown that the best results are achieved when both approaches are combined in a judicious way. Such joint sequential/parallel optimization preserves advantages of both treatments: adaptiveness (sequential approach) and robustness to abrupt changes (parallel approach).