Abstract
Most of the work on automatic detection tracking and classification of unmanned applications over the past twenty years has been focused on ground and aerial vehicles. Recently, the research has also focused on unmanned surface and underwater vehicles for autonomous capabilities.The ability to recognize and identify obstacles becomes more essential with USVs autonomous capabilities, such as obstacle avoidance, decision modules, and other Artificial Intelligence (AI) abilities using low cost sensors. This paper presents multi-target automatic algorithm stages to acquire, identify, and track targets from an Unmanned Surface Vehicle (USV) located in marine environments with LIDAR sensor challenging clutter. We present several clutter models and formulations to handle clutter phenomena. We propose the Probability Hypothesis Density (PHD) Bayes filter, challenging clutter for multi-target tracking.
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Gal, O., Zeitouni, E. (2013). Tracking Objects Using PHD Filter for USV Autonomous Capabilities. In: Sauzé, C., Finnis, J. (eds) Robotic Sailing 2012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33084-1_1
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DOI: https://doi.org/10.1007/978-3-642-33084-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33083-4
Online ISBN: 978-3-642-33084-1
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