Adaptive distributed Estimation Fusion algorithm based on the Consensus Averaging algorithm

This paper focuses on the distributed iterative parameter estimation scheme, based on the consensus averaging algorithm, for estimating an unknown parameter from the noisy measurements. A new spatio-temporal adaptive algorithm, called the consensus averaging-based adaptive estimation fusion (CA-AEF) algorithm is proposed, which accelerates the convergence rate of the current distributed iterative scheme. This algorithm models each node as an adaptive filter, and the performance improvement is achieved by introducing an adaptive weight updating method. Simulation results show that the proposed algorithm largely improves the convergence rate of the distributed parameter estimation, and also improve the estimation accuracy.