  This paper investigates the distributed Kalman filtering (DKF) from
distributed optimization viewpoint. Motivated by the fact that Kalman filtering
is a maximum a posteriori estimation (MAP) problem, which is a quadratic
optimization problem, we reformulate DKF problem as a consensus optimization
problem, resulting in that it can be solved by many existing distributed
optimization algorithms. A new DKF algorithm employing the dual ascent method
is proposed, and its stability is proved under mild assumptions. The
performance of the proposed algorithm is evaluated through numerical
experiments.
