  We consider the Kalman-filtering problem with multiple sensors which are
connected through a communication network. If all measurements are delivered to
one place called fusion center and processed together, we call the process
centralized Kalman-filtering (CKF). When there is no fusion center, each sensor
can also solve the problem by using local measurements and exchanging
information with its neighboring sensors, which is called distributed
Kalman-filtering (DKF). Noting that CKF problem is a maximum likelihood
estimation problem, which is a quadratic optimization problem, we reformulate
DKF problem as a consensus optimization problem, resulting in that DKF problem
can be solved by many existing distributed optimization algorithms. A new DKF
algorithm employing the distributed dual ascent method is provided and its
performance is evaluated through numerical experiments.
