An unbiased equation-error-based adaptive IIR filtering algorithm

We modify the off-line system identification procedure proposed by Regalia (1994) into an adaptive IIR filtering algorithm based on the stochastic gradient method. The proposed algorithm aims to minimize equation error, recursively, under a unit-norm constraint on the characteristic polynomial instead of the usual monic constraint. The unit-norm constraint eliminates the bias associated with equation error based estimates, when the additive measurement noise is white. The unit-norm constraint is enforced by adapting the parameters of the characteristic polynomial in (hyper)spherical coordinates. Simulation results indicate that the proposed algorithm provides estimates that are unbiased and that it is a computationally efficient alternative, for the same performance, to FIR adaptive filters