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A scanning LiDAR for long range detection and tracking of UAVs / Alain Quentel (2021)  

Public Titre : A scanning LiDAR for long range detection and tracking of UAVs Type de document : Thèse/HDR Auteurs : Alain Quentel, Auteur ; Yohan Dupuis, Directeur de thèse Editeur : Rouen : Université de Rouen Année de publication : 2021 Importance : 159 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le diplôme de Doctorat, spécialité Electronique, microélectronique, optique et lasers, optoélectronique microondes robotique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] drone [Termes IGN] optimisation (mathématiques) [Termes IGN] poursuite de cible [Termes IGN] réflectivité [Termes IGN] télémètre laser aéroporté [Termes IGN] télémétrie laser aéroporté [Termes IGN] temps de vol Index. décimale : THESE Thèses et HDR Résumé : (auteur) Misuse of civil drones, or UAVs (unmanned aerial vehicles) has been a rising concern in the past few years. As a response, multiple systems including optics, electronics and even acoustics technologies have been developed for detection and tracking. Unfortunately, UAVs represent a challenging target to detect and track due to their small, decimetric size and large variability of shapes and behaviors. In this PhD, we developed and optimized a LiDAR (light detection and ranging) system to tackle this issue to distances up to a kilometer. In our system, range is acquired using the time of flight principle, and imagery done by sequentially scanning the scene with a dual-axis galvanometer. We took advantage of the scanning versatility to develop several operating modes. A standard detection mode captures the image of the scene using a raster-scan of large field of view. Tracking mode is based on a local pattern surrounding the target, which is updated at a very high rate to keep the target within its boundaries. Efforts were put into a theoretical and numerical optimization study of the numerous parameters involved in our scanning LiDAR, so as to reach sufficient performances in term of maximal range, localization resolution and rate. Pattern optimization for both detection and tracking mode was a primary focus, using the target probability of detection as the function to maximize. Target size, speed and reflectivity was also introduced in the probability of detection, giving a complete overview of the system performance. On our LiDAR platform, developed from the ground up, each component was characterized to enrich and validate our models. This prototype was tested for UAVs detection and tracking during several weeks of trials. Following this success, a pre-industrial integration process was launched and supervised by the candidate. Numéro de notice : 28535 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de doctorat : Electronique, microélectronique, optique et lasers, optoélectronique microondes robotique : Rouen : 2021 Organisme de stage : Institut de Recherche en Systèmes Electroniques Embarqués DOI : sans En ligne : https://hal.science/tel-03228683 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99312