Abstract
A learning algorithm for the training of quaternion valued adaptive infinite impulse (IIR) filters is introduced. This is achieved by taking into account specific properties of stochastic gradient approximation in the quaternion domain and the recursive nature of the sensitivities within the IIR filter updates, to give the quaternion-valued stochastic gradient algorithm for adaptive IIR filtering (QSG-IIR). Further, to reduce computational complexity, a variant of the QSG-IIR is introduced, which for small stepsizes makes better use of the available information. Stability analysis and simulations on both synthetic and real world 4D data support the approach.