SELECTIVE PARTIAL UPDATE NORMALIZED LEAST MEAN SQUARE ALGORITHMS FOR DISTRIBUTED ESTIMATION OVER AN ADAPTIVE INCREMENTAL NETWORK

SELECTIVE PARTIAL UPDATE NORMALIZED LEAST MEAN SQUARE ALGORITHMS FOR DISTRIBUTED ESTIMATION OVER AN ADAPTIVE INCREMENTAL NETWORK

Selective partial update (SPU) strategy in adaptive filter algorithms is used to reduce the computational complexity. In this paper we apply the SPU Normalized Least Mean Squares algorithms (SPU-NLMS) for distributed estimation problem based on incremental strategy in a incremental network. The distributed SPU-NLMS (dSPUNLMS) reduces the computational complexity while it’s performance is close to the dNLMS. We demonstrate the good performance of dSPU-NLMS in both convergence speed and steady-state mean square error