| Titre : | Swarm AUVs: Target localization and tracking | | Type de document : | projet fin études | | Auteurs : | Rabab Boulouchgour, Auteur | | Langues : | Français (fre) | | Catégories : | BIG DATA
| | Mots-clés : | AUV, Swarm AUVs, Target Tracking, Deep learning | | Index. décimale : | mast 248/19 | | Résumé : | The underwater represents a very challenging environment to explore, the development
of autonomous underwater vehicles allowed accomplishing missions difficult if
not impossible for human divers. However, AUVs are met with other challenges such
as the mission time limited by batteries’ duration, thus the focus is now on developing
swarm AUVs who cooperate to accomplish tasks more efficiently.
The efficiency of a swarm navigation is constrained by the communication link and the
knowledge of the AUVs states, in this work we focus on the case of the master-slave architecture,
and we address the problem of the localization and tracking. The proposed
method is based on sparsely-allocated OFDM as a communication strategy and target
labeling protocol and LMHT-DeepMTT algorithm based on modified Hough transform
supported by DeepMTT a deep learning maneuvering tracking algorithm. Through
simulation, we evaluate the performance of the approach and find that it allows to
track the targets efficiently even in presence of important noise.
|
Swarm AUVs: Target localization and tracking [projet fin études] / Rabab Boulouchgour, Auteur . - [s.d.]. Langues : Français ( fre) | Catégories : | BIG DATA
| | Mots-clés : | AUV, Swarm AUVs, Target Tracking, Deep learning | | Index. décimale : | mast 248/19 | | Résumé : | The underwater represents a very challenging environment to explore, the development
of autonomous underwater vehicles allowed accomplishing missions difficult if
not impossible for human divers. However, AUVs are met with other challenges such
as the mission time limited by batteries’ duration, thus the focus is now on developing
swarm AUVs who cooperate to accomplish tasks more efficiently.
The efficiency of a swarm navigation is constrained by the communication link and the
knowledge of the AUVs states, in this work we focus on the case of the master-slave architecture,
and we address the problem of the localization and tracking. The proposed
method is based on sparsely-allocated OFDM as a communication strategy and target
labeling protocol and LMHT-DeepMTT algorithm based on modified Hough transform
supported by DeepMTT a deep learning maneuvering tracking algorithm. Through
simulation, we evaluate the performance of the approach and find that it allows to
track the targets efficiently even in presence of important noise.
|
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Swarm AUVs: Target localization and tracking / Rabab Boulouchgour
