Abstract The problem of identification/tracking of quasi-periodically varying complex systems is considered. This problem is a generalization, to the system case, of a classical signal processing task of either elimination or extraction of nonstationary sinusoidal signals buried in noise. The proposed solution is based on the exponentially weighted basis function (EWBF) approach. First, the basic EWBF algorithm is derived. Then its frequency-decoupled, parallel-form and cascade-form variants, with highly modular structure and reduced computational requirements, are described. Finally, the frequency-adaptive versions of all schemes are obtained using the recursive prediction error method.