A fast convergence algorithm for sparse-tap adaptive FIR filters for an unknown number of multiple echoes

This paper proposes a fast convergence algorithm for sparse-tap adaptive FIR filters for an unknown number of multiple echoes. Coefficient values and tap-positions are simultaneously controlled. A constrained region for new-tap positions is selected from equisize subgroups of all possible tap-positions and it hops from one subgroup to another to cover multiple echoes. The hopping order and the staying time for each subgroup are adaptively determined based on the absolute coefficient values. Simulation results with colored signals show that the proposed algorithm saves more than 80% in the convergence time over the full-tap NLMS and 50% over the STWQ. Tracking capability of the proposed algorithm exhibits its superior characteristics