Adaptive IIR filter initialization via hybrid FIR/IIR adaptive filter combination

A novel hybrid adaptive FIR/IIR filter configuration is presented. The aim is to reduce the main obstacles limiting the use of adaptive IIR filters: namely convergence and stability. The hybrid scheme attempts to exploit the good qualities of both system adaptive FIR filters (good convergence and stability) and those for adaptive IIR filters (sharp transition bands, lower order, and complexity). In this paper, the hybrid scheme is presented for a system identification problem, and the task is achieved in three main steps: (a) A training signal is applied to an adaptive FIR filter to achieve a near optimum approximation; (b) the FIR coefficients are mapped through the balanced model reduction technique to a smaller set of coefficients initializing an adaptive IIR filter; (c) there is a process of fine tuning (small adaptive stepsize) the adaptive IIR coefficients for further approximation precision. The IIR filter in the hybrid scheme makes use of the Steiglitz McBride algorithm that assures the stability of the filter. Furthermore, the convergence to the minimum error performance is enforced by the fact that the adaptive IIR filter is operating close to the optimum, requiring very small pole perturbation. The hybrid scheme is compared against the adaptive IIR filter initialized with a set of zero coefficients.