Learning of scanning strategies for ...
Type de document :
Communication dans un congrès avec actes
Titre :
Learning of scanning strategies for electronic support using predictive state representations
Auteur(s) :
Glaude, Hadrien [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Thales Airborne Systems
Enderli, Cyrille [Auteur]
Thales Airborne Systems
Grandin, Jean-François [Auteur]
Thales Airborne Systems
Pietquin, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille, Sciences et Technologies
Institut universitaire de France [IUF]
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Thales Airborne Systems
Enderli, Cyrille [Auteur]
Thales Airborne Systems
Grandin, Jean-François [Auteur]
Thales Airborne Systems
Pietquin, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille, Sciences et Technologies
Institut universitaire de France [IUF]
Sequential Learning [SEQUEL]
Titre de la manifestation scientifique :
International Workshop on Machine Learning for Signal Processing (MLSP 2015)
Ville :
Boston
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2015-09-17
Titre de la revue :
Proceedings of the International Workshop on Machine Learning for Signal Processing
Date de publication :
2015
Mot(s)-clé(s) en anglais :
Index Terms— Electronic support
super heterodyne
sensor scheduling
predictive state representation
subspace identification
super heterodyne
sensor scheduling
predictive state representation
subspace identification
Discipline(s) HAL :
Informatique [cs]/Apprentissage [cs.LG]
Résumé en anglais : [en]
In Electronic Support, a receiver must monitor a wide frequency spectrum in which threatening emitters operate. A common approach is to use sensors with high sensitivity but a narrow band-width. To maintain surveillance ...
Lire la suite >In Electronic Support, a receiver must monitor a wide frequency spectrum in which threatening emitters operate. A common approach is to use sensors with high sensitivity but a narrow band-width. To maintain surveillance over the whole spectrum, the sensor has to sweep between frequency bands but requires a scanning strategy. Search strategies are usually designed prior to the mission using an approximate knowledge of illumination patterns. This often results in open-loop policies that cannot take advantage of previous observations. As pointed out in past researches, these strategies lack of robustness to the prior. We propose a new closed loop search strategy that learns a stochastic model of each radar using predic-tive state representations. The learning algorithm benefits from the recent advances in spectral learning and rank minimization using nuclear norm penalization.Lire moins >
Lire la suite >In Electronic Support, a receiver must monitor a wide frequency spectrum in which threatening emitters operate. A common approach is to use sensors with high sensitivity but a narrow band-width. To maintain surveillance over the whole spectrum, the sensor has to sweep between frequency bands but requires a scanning strategy. Search strategies are usually designed prior to the mission using an approximate knowledge of illumination patterns. This often results in open-loop policies that cannot take advantage of previous observations. As pointed out in past researches, these strategies lack of robustness to the prior. We propose a new closed loop search strategy that learns a stochastic model of each radar using predic-tive state representations. The learning algorithm benefits from the recent advances in spectral learning and rank minimization using nuclear norm penalization.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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