  Fueled by applications in sensor networks, these years have witnessed a surge
of interest in distributed estimation and filtering. A new approach is hereby
proposed for the Distributed Kalman Filter (DKF) by integrating a local
covariance computation scheme. Compared to existing well-established DKF
methods, the virtue of the present approach lies in accelerating the
convergence of the state estimates to those of the Centralized Kalman Filter
(CKF). Meanwhile, an algorithm is proposed that allows each node to compute the
averaged measurement noise covariance matrix within a minimal discrete-time
running steps in a distributed way. Both theoretical analysis and extensive
numerical simulations are conducted to show the feasibility and superiority of
the proposed method.
