Performance Study of Partial Update Adaptive Filter Algorithms in Distributed Networks

Selective partial update (SPU) strategy in adaptive filter algorithms is used to reduce the computational complexity. In this paper we apply the SPU Normalized LeastMean Squares algorithms (SPU-NLMS) for distributed estimation problem based on incremental strategy in incremental network. The distributed SPU-NLMS (dSPUNLMS) reduce 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

Keywords:

Selective partial update , normalized least mean squares , distributed estimation. Incremental network