期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 50, 期 10, 页码 4346-4357出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2901515
关键词
Kalman filters; Target tracking; Robot sensing systems; Mutual information; Optimal control; State estimation; Event-trigger; mixture Kalman filter; mobile sensors; mutual information theory; state estimation
类别
资金
- National Natural Science Foundation of China [61773289, U1713211, 61733013, U1764261, 61673178]
- Shanghai International Science and Technology Cooperation Project [18510711100]
- Shanghai Shuguang Project [16SG28, 18SG18]
- Shanghai Natural Science Foundation [17ZR1444700, 17ZR1445800]
- Fundamental Research Funds for the Central Universities
In this paper, the distributed remote state estimation problem for conditional dynamic linear systems in mobile sensor networks with an event-triggered mechanism is investigated. The distributed mixture Kalman filtering method is proposed to track the state of the maneuvering target, which uses particle filtering to estimate the nonlinear variables and apply Kalman filtering to estimate the linear variables. An event-based distributed filtering scheme is designed, which is an energy-efficient way to transmit data between sensors and estimators. In addition, by using the mutual information theory, an optimal control problem is formed to control the position of sensors so that the target tracking process can be achieved quickly. Finally, a simulation example about the maneuvering target tracking is provided to corroborate the effectiveness of the filtering method and the control performance for sensors.
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