Combination of adaptive filters with coefficients feedback

In parallel combinations of adaptive filters, the component filters are usually run independently to be later on combined, leading to a stagnation phase before reaching a lower error. Conditional transfers of coefficients between the filters have been introduced in an attempt to address this issue. The present work proposes a more natural way of accelerating the convergence to steady-state, using a cyclic feedback of the overall weights to all component filters, instead of a unidirectional conditional transfer. It is shown that, depending on the cycle length, the resulting recursion is equivalent to either: (i) the independent combination, (ii) a variable step size adaptive filter, or (iii) a new hybrid algorithm. Comments on the universality of the approach are presented along with a technique to design the cycle length. Comparisons in stationary and non-stationary system identification scenarios demonstrate the superior performance of this new combination method.