Adaptive fast consensus algorithm for distributed sensor fusion

In the past few years, the problem of distributed parameter estimation has received a lot of attention, particularly for the applications in sensor networks. This paper focuses on the distributed iterative parameter estimation scheme. An alternative form of the consensus averaging-based algorithm is introduced, in which each node iteratively updates its estimate by adding a weighted sum of its own and its neighbors' estimate, with the time-varying weight matrices. To improve the convergence of this distributed iterative scheme, an adaptive weight matrix modification algorithm is proposed, in which each node is modeled as an adaptive filter. The weight matrix is modified using the steepest descent method. With the non-persistent measurement data, a linear predictor is designed to provide the reference signal in the adaptive filter. As a result, the intermediate estimate of each node is driven closer to the steady-state estimate. Simulation results show that the proposed algorithm decreases the intermediate estimation error and accelerates the consensus convergence, with the nearly optimal steady-state estimation performance. (C) 2009 Elsevier B.V. All rights reserved.