CLC number: TP273.5
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-09-11
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Citations: Bibtex RefMan EndNote GB/T7714
Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu. Recent advances in multisensor multitarget tracking using random finite set[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 5-24.
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author="Kai Da, Tiancheng Li, Yongfeng Zhu, Hongqi Fan, Qiang Fu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="1",
pages="5-24",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000266"
}
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Abstract: In this study, we provide an overview of recent advances in multisensor multitarget tracking based on the random finite set (RFS) approach. The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements and posterior densities between sensors, respectively. Important properties of each fusion rule including the optimality and sub-optimality are presented. In particular, two robust multitarget density-averaging approaches, arithmetic- and geometric-average fusion, are addressed in detail for various RFSs. Relevant research topics and remaining challenges are highlighted.
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