Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs
Systems and Control
2022-08-22 v1 Systems and Control
Optimization and Control
Abstract
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.
Cite
@article{arxiv.2208.09328,
title = {Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs},
author = {Kunhee Ryu and Juhoon Back},
journal= {arXiv preprint arXiv:2208.09328},
year = {2022}
}
Comments
This is a preprint submitted to Automatica