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Predicting AIS reception using tropospheric propagation forecast and machine learning

Abstract : The aim of this paper is to present a methodology for modelling and predicting the coverage of an Automatic Identification System (AIS) station based on tropospheric index forecast maps and modelling methods from machine learning. The aim of this work is to cartographically represent the areas in which the AIS signals emitted by ships will be received by a coastal station. This work contributes to the improvement of maritime situational awareness and to the detection of anomalies at sea [1], and in particular to the identification of AIS message falsifications [2] (ubiquity of a vessel by identity theft, falsification of GPS positions and deactivation of AIS).
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03726782
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Submitted on : Monday, July 18, 2022 - 7:16:20 PM
Last modification on : Monday, September 12, 2022 - 4:53:10 PM

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  • HAL Id : hal-03726782, version 1

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Zackary Vanche, Ambroise Renaud, Aldo Napoli. Predicting AIS reception using tropospheric propagation forecast and machine learning. IEEE AP-S/URSI International Symposium on Antennas & Propagation (ISAP) 2022, Jul 2022, Denver, United States. ⟨hal-03726782⟩

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