Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques
arXiv:2011.11338 · doi:10.1109/MAES.2021.3070884
Abstract
Maritime surveillance (MS) is of paramount importance for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since ground-based radars and automatic identification system (AIS) do not always provide a comprehensive and seamless coverage of the entire maritime domain, the use of space-based sensors is crucial to complement them. We reviewed space-based technologies for MS in the first part of this work, titled "Space-based Global Maritime Surveillance. Part I: Satellite Technologies" [1]. However, future MS systems combining multiple terrestrial and space-based sensors with additional information sources will require dedicated artificial intelligence and data fusion techniques for the processing of raw satellite images and fuse heterogeneous information. The second part of our work focuses on the most promising artificial intelligence and data fusion techniques for MS using space-based sensors.
arXiv commentThis paper has been submitted to IEEE Aerospace and Electronic Systems Magazine
References in corpus (5)
- A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors
- A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams
- Space-based Global Maritime Surveillance. Part I: Satellite Technologies
- Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction
- Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm