The optimal distributed filtering for the coupled systems with random delay

Cai, Yunze; Wang, O. Hua; Xu, Xiaoming

doi:10.1007/s11071-012-0588-7

Cited by 3 publications

(1 citation statement)

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“…The distributed algorithms can solve this problem by processing the data in distributed multiple servers. Various algorithms for multiplerobot localization have been developed [13]- [24], and especially, in [13], the problem of the distributed localization of multiple mobile robots when the communication link with neighboring nodes is temporarily lost was studied. In addition, machine learning (ML)-based localization algorithms for WSNs were developed to obtain an accurate location and minimize the adverse effects of internal or external factors [25].…”

Section: Introductionmentioning

confidence: 99%

Distributed Finite Memory Estimation From Relative Measurements for Multiple-Robot Localization in Wireless Sensor Networks

et al. 2022

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Mobile robot localizations have been extensively studied, and various algorithms for multiple-robot localization have been developed. However, existing methods for multiple-robot localization often exhibit poor performance under harsh conditions, such as missing measurements and sudden appearance of obstacles. To overcome this problem, this paper proposes a novel method for multiple-robot localization in wireless sensor networks. The proposed method is theoretically based on the finite memory estimation and utilizes relative distance and angle measurements between robots. Thus, the proposed method is referred to as distributed finite memory estimation from relative measurements (DFMERM). Due to the finite memory structure, the DFMERM has inherent robustness against computational and modeling errors. Moreover, the novel distributed localization method using relative measurements shows the robustness against missing measurements. Robust DFMERM localization performance is experimentally demonstrated using multiple mobile robots under the harsh conditions.

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Section: Introductionmentioning

confidence: 99%