Bayesian information fusion and multitarget tracking for maritime situational awareness

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...

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Bibliographic Details
Published in:IET radar, sonar & navigation Vol. 14; no. 12; pp. 1845 - 1857
Main Authors: Gaglione, Domenico, Soldi, Giovanni, Meyer, Florian, Hlawatsch, Franz, Braca, Paolo, Farina, Alfonso, Win, Moe Z
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.12.2020
Subjects:
authors
Bayes methods
Bayesian framework
Bayesian information fusion
central sensing modality
coastal areas
context‐based information
dedicated information fusion
geophysical signal processing
HFSW radar data
high‐frequency surface‐wave radars
information fusion algorithms
loopy sum‐product algorithm
low environmental footprint
low power requirements
marine radar
maritime situational awareness
MSA applications
multisensor‐multitarget tracking problem
oceanographic techniques
over‐the‐horizon distances
radar detection
radar tracking
Research Article
sea areas
sensor fusion
ship traffic
ships
suitably devised factor graph
target tracking
tracking algorithms
wide‐area operational picture
oceanographic techniques
radar detection
dedicated information fusion
loopy sum-product algorithm
information fusion algorithms
sensor fusion
context-based information
sea areas
tracking algorithms
Bayesian information fusion
coastal areas
ship traffic
multisensor-multitarget tracking problem
high-frequency surface-wave radars
MSA applications
HFSW radar data
marine radar
radar tracking
low power requirements
Bayesian framework
geophysical signal processing
low environmental footprint
maritime situational awareness
wide-area operational picture
ships
target tracking
central sensing modality
Bayes methods
suitably devised factor graph
over-the-horizon distances
authors
ISSN:1751-8784, 1751-8792
Online Access:Get full text
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Description
Summary: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.
ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2019.0508