Local-Bearing-Based Prescribed-Time Distributed Localization of Multiagent Systems With Noisy Measurement

This work investigates stability and localizability of local-bearing-based multiagent systems without common orientation in the presence of measurement noise, which are more general but also more challenging to deal with than global-bearing-based multiagent systems under ideal environment. Based on...

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Vydáno v:IEEE transactions on industrial informatics Ročník 20; číslo 7; s. 9518 - 9526
Hlavní autoři: Lv, Yunkai, Wu, Zeming, Zhang, Hao, Wang, Zhuping, Yan, Huaicheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Controllability
Convergence
Effectiveness
Estimation
Local bearing
Localization
localization estimation
Location awareness
measurement noise
Multi-agent systems
Multiagent systems
Noise measurement
Orientation
orientation estimation
prescribed-time
Robustness
Time measurement
ISSN:1551-3203, 1941-0050
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Shrnutí:This work investigates stability and localizability of local-bearing-based multiagent systems without common orientation in the presence of measurement noise, which are more general but also more challenging to deal with than global-bearing-based multiagent systems under ideal environment. Based on local-bearing unbiased estimator constructed from the historical information and a newly designed time-varying gain, a robust prescribed-time orientation estimation algorithm is proposed to ensure that the local reference frame of the follower agent is aligned with the global one. The local bearing information is more easily obtained than global one. Therefore, the new orientation estimation result is expected to be more widely applicable. The robust orientation estimation algorithm is then applied to the problem of localization estimation, and a prescribed-time distributed localization estimation algorithm is developed. The distinctive advantage of this work is that only the local bearing information is used, the fast and controllable localization estimation is achieved. The global convergence is derived based on the cascade system. Some simulation and experiment results are provided to prove the effectiveness of the proposed estimation algorithms.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3383518