@inproceedings{425d77a7588347d3ab36ea08dc2c1b78,
title = "Decentralized Collaborative Localization Based on Iterated Kalman Filter Using Relative and Absolute Observations",
abstract = "This paper proposes a robust algorithm which can realize distributed computing for the problem of multi-agent collaborative localization using relative and absolute observations. Firstly, the relative measurement model of agents is approximated by taking the state of their neighbors as prior knowledge, the approximation error can be modeled as the Gaussian distribution. This is very critical for the algorithm to achieve decentralized computing. Then the iterated kalman filtering algorithm is used to estimate the state for each agent using the information of itself and its neighbors. Finally, the proposed algorithm is compared with other existing approaches. Simulation results show that our algorithm provides better performance in positioning accuracy.",
keywords = "Decentralized collaborative localization, Iterated Kalman filter, Multi-agent system, Relative observation",
author = "Kuo Tu and Huixia Liu and Jinwen Hu and Chunhui Zhao and Zhao Xu and Xiaolei Hou and Yongping Zhang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Autonomous Unmanned Systems, ICAUS 2021 ; Conference date: 24-09-2021 Through 26-09-2021",
year = "2022",
doi = "10.1007/978-981-16-9492-9\_86",
language = "英语",
isbn = "9789811694912",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "871--880",
editor = "Meiping Wu and Yifeng Niu and Mancang Gu and Jin Cheng",
booktitle = "Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021",
}