New sparse adaptive algorithms using partial update

In this paper, we propose two new sparse adaptive filtering algorithms using partial update. By taking advantage of both impulse response sparseness and partial update, we design different criteria to determine which coefficients to be updated in order to improve the performance of typical partial update algorithms. Compared with the normalized least mean square (NLMS), selective partial update NLMS (SPUNLMS) and proportionate NLMS (PNLMS++) algorithm, the proposed partial update sparse NLMS (PSNLMS) algorithms achieve faster convergence speed with even less computational complexity. Simulation results show that they perform well in applications where identification of long sparse impulse responses is needed. Network echo cancellation is a typical example.