Distributed Kalman filtering with minimum-time consensus algorithm
For sensor network applications requiring distributed estimation, this method improves convergence speed of distributed Kalman filters.
This paper proposes a Distributed Kalman Filter that accelerates convergence to centralized estimates by integrating a local covariance computation scheme and a minimum-time consensus algorithm for averaging noise covariance matrices. Simulations demonstrate faster convergence compared to existing methods.
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.