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Bayesian information fusion and multitarget tracking for maritime situational awareness
IET Radar Sonar and Navigation ( IF 1.7 ) Pub Date : 2020-11-30 , DOI: 10.1049/iet-rsn.2019.0508
Domenico Gaglione 1 , Giovanni Soldi 1 , Florian Meyer 2 , Franz Hlawatsch 3 , Paolo Braca 1 , Alfonso Farina 4 , Moe Z. Win 5
Affiliation  

The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.

中文翻译:

贝叶斯信息融合和多目标跟踪提高了海上态势感知

海事态势感知(MSA)的目标是实时提供沿海地区和海洋中船舶运输的无缝广域运行画面。雷达是MSA的中央感应方式。尤其是,海洋高频表面波(HFSW)雷达因其低环境足迹和低功率要求而非常适合在超视距距离上测量大型海域。但是,对于在MSA应用程序中普遍存在的挑战性条件,它们的设计并不是最佳的,因此需要开发专用的信息融合和多传感器多目标跟踪算法。在这个研究中,作者展示了如何在贝叶斯框架中提出多传感器多目标跟踪问题,以及如何通过在适当设计的因子图上运行循环和乘积算法来有效地解决多传感器多目标跟踪问题。与先前提出的方法相比,该方法在估计精度,计算复杂度,实现灵活性和可伸缩性方面是有利的。此外,可以通过在线估计未知模型参数以及融合自动识别系统(AIS)数据和基于上下文的信息来进一步增强其性能。通过基于模拟数据以及真实HFSW雷达数据和真实AIS数据的实验结果,证明了所提出的贝叶斯多传感器多目标跟踪和信息融合算法的有效性。就估计精度,计算复杂性,实现灵活性和可伸缩性而言,该方法是有利的。此外,可以通过在线估计未知模型参数以及融合自动识别系统(AIS)数据和基于上下文的信息来进一步增强其性能。通过基于模拟数据以及真实HFSW雷达数据和真实AIS数据的实验结果,证明了所提出的贝叶斯多传感器多目标跟踪和信息融合算法的有效性。就估计精度,计算复杂性,实现灵活性和可伸缩性而言,该方法是有利的。此外,可以通过在线估计未知模型参数以及融合自动识别系统(AIS)数据和基于上下文的信息来进一步增强其性能。通过基于模拟数据以及真实HFSW雷达数据和真实AIS数据的实验结果,证明了所提出的贝叶斯多传感器多目标跟踪和信息融合算法的有效性。通过以在线方式估计未知模型参数以及融合自动识别系统(AIS)数据和基于上下文的信息,可以进一步提高其性能。通过基于模拟数据以及真实HFSW雷达数据和真实AIS数据的实验结果,证明了所提出的贝叶斯多传感器多目标跟踪和信息融合算法的有效性。通过以在线方式估计未知模型参数以及融合自动识别系统(AIS)数据和基于上下文的信息,可以进一步提高其性能。通过基于模拟数据以及真实HFSW雷达数据和真实AIS数据的实验结果,证明了所提出的贝叶斯多传感器多目标跟踪和信息融合算法的有效性。
更新日期:2020-12-01
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