Performance and robustness evaluation of dc-link stabilization

Mosskull, Henrik

doi:10.1016/j.conengprac.2015.06.011

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…The majority of these works assume that at least in theory, the trajectory of a ship can be approximated as a straight line for a short time interval. Therefore, linear interpolation is the most widely used gap-filling method [32][33][34][35]. According to this method, the positions of the ship between two points are calculated by interpolating their coordinates (latitude and longitude) according to the desired timestamp.…”

Section: Incomplete Trajectoriesmentioning

confidence: 99%

The Big Picture: An Improved Method for Mapping Shipping Activities

Troupiotis-Kapeliaris,

Zissis,

Bereta

et al. 2023

Remote Sensing

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Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.

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Section: Incomplete Trajectoriesmentioning

confidence: 99%