论文标题
分布式卡尔曼过滤的共识优化方法：通过证明的集中式过滤的性能恢复
Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs
论文作者
论文摘要
本文从分布式优化观点研究了分布式的卡尔曼过滤（DKF）。由于Kalman过滤是最大的后验估计（MAP）问题，这是一个二次优化问题，我们将DKF问题重新制定为共识优化问题，从而将其通过许多现有的分布式优化算法来解决。提出了一种采用双重上升方法的新的DKF算法，并在轻度假设下证明了其稳定性。通过数值实验评估所提出的算法的性能。
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.
