Journal
SYSTEMS & CONTROL LETTERS
Volume 131, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.sysconle.2019.104500
Keywords
Distributed fusion; Kalman filter; Sensor network
Funding
- National Natural Science Foundation of China [91748128, 61503333, 11802268]
- Fundamental Research Funds for the Central Universities, China [2019QNA4028]
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In this paper, we study the distributed Kalman filtering for sensor network with mild assumption on communication topology and local observability. To this end, a new peer-to-peer distributed Kalman filtering is proposed, where each sensor communicates with its connected neighbors to achieve average consensus on weighted measurements and inverse-covariance matrices. Then, a consensus strategy is introduced to reduce the error produced by embedded dynamic consensus method. In addition, the convergence and steady-state performance of the proposed algorithm are also investigated, and we prove rigorously that the biased estimates are bounded and controllable. Numerical simulations validate the theoretical contributions of this paper. (C) 2019 Elsevier B.V. All rights reserved.
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