This research note highlights the challenges of detecting and tracking “dark ships” involved in illegal activities within a nation’s Exclusive Economic Zone (EEZ), particularly in the Indian Ocean region. These ships often turn off their Automatic Identification System (AIS) transponders to avoid detection. The note explores various current state-of-the-art technologies for detecting dark ships, including Synthetic Aperture Radar image analysis, AIS data analysis, Visible Infrared Imaging Radiometer Suite, radio frequency analysis, and Passive Oceanic Acoustic Waveguide Remote Sensing. The note also highlights the challenges of the underwater channel and underwater radiated noise, as well as the unique signatures of each vessel’s noise sources.The note highlights the need for developing software to detect dark ships via identifying discrepancies in the real-time noise level obtained from an underwater sensor network and in the spatio-temporal low-frequency noise map of the Indian Ocean region.
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1. The detection of dark ships (vessels involved in illegal activities that turn off their AIS transponders) is a significant challenge for maritime security.
2. Current state-of-the-art technologies for detecting dark ships include AIS data analysis, synthetic aperture radar image analysis, visible infrared imaging radiometer suite, radio frequency analysis, and passive oceanic acoustic waveguide remote sensing.
3. Underwater hydrophones are used for passive acoustic detection of underwater radiated noise (URN) from ships, which can be used to identify and track vessels based on their unique acoustic signature.
4. Developing a software for detecting dark ships in the Indian Ocean region via identifying discrepancies in the real-time noise level obtained from an underwater sensor network should be pursued.
“”There is a visible lack of a method specific to the Indian Ocean Region which can detect surface as well as sub-surface vessels that can work accurately in real time so as to provide actionable information.
Research Intern, MRC