Distributed Kalman-filtering: Distributed optimization viewpoint
For researchers in sensor networks and distributed estimation, this provides a new perspective linking Kalman filtering to distributed optimization, though the approach is incremental.
The paper reformulates distributed Kalman-filtering as a consensus optimization problem, enabling the use of distributed optimization algorithms. A new algorithm using distributed dual ascent is proposed and evaluated numerically.
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.