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Distributed Estimation for Interconnected Dynamic Systems Under Binary Sensors
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AI
DOI：10.1109/JSEN.2022.3176911.png)
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摘要
En 中文
This paper is concerned with the distributed state estimation problem for a class of interconnected dynamic systems, where several binary sensors are deployed to observe each subsystem. The judgement of switching instant for binary sensor is based on the certain estimates and sensed variables. In this case, two innovation sequences which combine thresholds and state estimates are given to extract valid information from binary measurement. Then, a distributed recursive estimator is given on the basis of weighting fusion criteria and bounded recursive optimization. The interconnected gains of state estimator are designed by minimizing the impacts of neighboring terms, while the optimal local gains and weighting fusion matrices are designed by constructing convex optimization problems. Moreover, the asymptotic stability of squared estimation error is proved by means of sequential analysis. Finally, two illustrative examples are employed to show the effectiveness of the proposed methods.
Keyword:
Sensors
Estimation
Symmetric matrices
Sparse matrices
Kalman filters
Switches
Sensor systems
Interconnected systems
distributed estimation
binary sensors
bounded noises
期刊
IF:
4.5
论文数:
2.1W
被引数:
7.3W
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