Bias-free adaptive IIR filtering

We present a new family of algorithms that solve the bias problem in the eq uation-error based adaptive infinite impulse response (IIR) filtering. A no vel constraint, called the constant-norm constraint, unifies the quadratic constraint and the monic one. By imposing the monic constraint on the mean square error (MSE) optimization, the merits of both constraints are inherit ed and the shortcomings are overcome. A new cost function based on the cons tant-norm constraint and Lagrange multiplier is defined. Minimizing the cos t function gives birth to a new family of bias-free adaptive IIR filtering algorithms. For example, two efficient algorithms belonging to the family a re proposed. The analysis of the stationary points is presented to show tha t the proposed methods can indeed produce bias-free parameter estimates in the presence of white noise. The simulation results demonstrate that the pr oposed methods indeed produce unbiased parameter estimation, while being si mple both in computation and implementation.