Least mean mixed-norm adaptive filtering

A new family of stochastic gradient adaptive filter algorithms is proposed which is based on mixed error norms. These algorithms combine the advantages of different error norms, for example the conventional, relatively well-behaved, least mean square algorithm and the more sensitive, but better converging, least mean fourth algorithm. A mixing parameter is included which controls the proportions of the error norms and offers an extra degree of freedom within the adaptation. A system identification simulation is used to demonstrate the performance of a least mean mixed-norm (square and fourth) algorithm