Bakr Muhammad Abu, Lee Sukhan
Intelligent Systems Research Institute, Sungkyunkwan University, Suwon, Gyeonggi-do 440-746, Korea.
Sensors (Basel). 2017 Oct 27;17(11):2472. doi: 10.3390/s17112472.
The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.
随着传感器和通信技术的进步，多传感器数据融合范式已从集中式架构演变为分散式或分布式架构。如今，分布式状态估计和数据融合因其在灵活性、对故障的鲁棒性以及基础设施和通信方面的成本效益方面优于集中式架构，已在工程和控制的各个领域得到广泛探索。然而，分布式多传感器数据融合并非没有技术挑战需要克服：即处理状态估计和传感器数据之间的互相关性和不一致性。在本文中，我们回顾了迄今为止可用的分布式多传感器数据融合的关键理论和方法，特别关注处理未知相关性和数据不一致性。我们旨在通过对其含义进行形式化分析，为读者提供一个超越个别理论和方法的统一视角。最后，强调了未来研究的几个方向。
