Diffusion normalized subband adaptive algorithm for distributed estimation employing signed regressor of input signal

Abstract This paper presents a novel diffusion subband adaptive filtering algorithm for distributed estimation over networks. To achieve the low computational load, the signed regressor (SR) approach is applied to normalized subband adaptive filter (NSAF) and two algorithms for diffusion networks are established. The diffusion SR-NSAF (DSR-NSAF) and modified DSR-NSAF (MDSR-NSAF) have fast convergence speed and low steady-state error similar to the conventional DNSAF. In addition, the proposed algorithms have lower computational complexity than DNSAF due to the signed regressor of the network input signals at each node. Also, based on the spatial-temporal energy conservation relation, the mean-square performance of DSR-NSAF is analyzed and the expressions for the theoretical learning curve and steady-state error are derived. The good performance of these algorithms and the validity of the theoretical results are demonstrated by presenting several simulation results.

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