@inproceedings{d1e9060a423148f086712666438c5998,
title = "Distributed extended kalman-consensus filtering algorithm based on sensor network for nonlinear system",
abstract = "Distributed state estimation (DSE) in collaborative tracking realm has received considerable attention in recent years. Conventional centralized filtering algorithms have encountered a series of problems, such as the poor robustness and the high communication overhead. To deal with these problems, this paper studies the distributed state estimation algorithm over mobile sensor networks. The focus is on developing a consensus-based distributed filtering algorithm, which named extended Kalman-consensus filter (EKCF) algorithm. The EKCF algorithm is consist of two stages. During the first stage, each sensor uses local information to obtain the state estimation based on extended Kalman filtering algorithm. In the second stage, each sensor communicates with its neighbors by performing the consensus algorithm. The sufficient conditions and the detailed proof for the boundedness of the EKCF is proposed. Simulation results of the formation tracking model verifies the effectiveness of EKCF algorithm is proposed in this paper.",
keywords = "Extended Kalman-consensus filter, Formation tracking model, Sensor network, State estimation",
author = "Zheng Zhang and Xiwang Dong and Qingke Tan and Yuan Liang and Qingdong Li and Zhang Ren",
note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "5",
doi = "10.23919/ChiCC.2018.8482664",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6977--6982",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
address = "美国",
}