Partial-update NLMS algorithms with data-selective updating

Partial-update adaptive filtering algorithms only update part of the filter coefficients at each time instant, leading to reduced computational complexity as compared with their conventional counterparts. In this paper, the ideas of the partial-update NLMS-type algorithms found in the literature are extended to the framework of set-membership filtering, from which data-selective NLMS type of algorithms with partial update are derived. The new algorithms combine data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. Simulation results verify the good performance of the new algorithms in terms of convergence speed, final misadjustment, and reduced computational complexity.