Bayesian information fusion and multitarget tracking for maritime situational awareness
Name
Radar-Survey-IET-R1-V06-1.pdf
Description
Accepted version
Size
7.09 MB
Format
Adobe PDF
Checksum (MD5)
dbc0dc1f30cc55db604ee495c4fcfee6
Author(s)
Gaglione, Domenico
Soldi, Giovanni
Meyer, Florian
Hlawatsch, Franz
Braca, Paolo
Farina, Alfonso
Win, Moe Z
Date Issued
2020
Journal
IET Radar Sonar and Navigation
Publisher
Institution of Engineering and Technology (IET)
Version
Author's final manuscript
Abstract
© The Institution of Engineering and Technology 2020. 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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1049/IET-RSN.2019.0508
