Asymptotically optimal consensus-based distributed filtering of continuous-time linear systems

被引：31
作者：
Battilotti, Stefano [1 ]
Cacace, Filippo [2 ]
d'Angelo, Massimiliano [1 ]
Germani, Alfredo [3 ]
机构：
[1] Univ Sapienza Roma, DIAG, Rome, Italy
[2] Univ Campus Biomed Roma, Ctr Integrato Ric, Rome, Italy
[3] Univ Aquila, DISIM, Laquila, Italy
来源：
AUTOMATICA | 2020年 / 122卷
关键词：
Continuous-time filters; Kalman filtering; Distributed filtering; Consensus filters; KALMAN CONSENSUS;
D O I：
10.1016/j.automatica.2020.109189
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We describe a consensus-based distributed filtering algorithm for linear systems with a parametrized gain and show that when the parameter becomes large the error covariance at each node becomes arbitrarily close to the error covariance of the optimal centralized Kalman filter. The result concerns distributed estimation over a connected un-directed or directed graph and for static configurations it only requires to exchange the estimates among adjacent nodes. A comparison with related approaches confirms the theoretical results and shows that the method can be applied to a wide range of distributed estimation problems. (c) 2020 Elsevier Ltd. All rights reserved.
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页数：7
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