Towards adaptive split spectrum processing

New results relevant for adaptive split spectrum processing (SSP) for ultrasonic clutter suppression are reported. Joint optimization of the filter bank and the nonlinearity is considered as well as how SSP can be considered performing maximum likelihood estimation. The joint optimization is realized by means of an artificial neural network which is trained using supervised learning like in system identification. The maximum likelihood approach is based on a matrix formulation of the filter bank processing.