Efficient adaptive DFT-domain Volterra filters using an automatically controlled number of quadratic kernel diagonals

This paper presents a method for estimating the optimum number of second-order kernel diagonals of an adaptive Volterra filter in system identification tasks. To this end, a recently proposed time-domain mechanism is carried over to the very efficient partitioned-block DFT-domain Volterra filtering technique. The size of the nonlinear memory is controlled by monitoring the performance of an adaptive combination scheme with two differently-sized quadratic kernels. Subsequently, an efficient version is derived, requiring only minor additional computations as compared to a single Volterra filter. The effectiveness of the outlined estimation procedure is demonstrated by various simulations with real nonlinear systems and both noise and speech inputs in an acoustic echo cancellation scenario.