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Learning of scanning strategies for ...
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Document type :
Communication dans un congrès avec actes
Title :
Learning of scanning strategies for electronic support using predictive state representations
Author(s) :
Glaude, Hadrien [Auteur]
Thales Airborne Systems
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Enderli, Cyrille [Auteur]
Thales Airborne Systems
Grandin, Jean-François [Auteur]
Thales Airborne Systems
Pietquin, Olivier [Auteur]
Sequential Learning [SEQUEL]
Institut Universitaire de France [IUF]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Conference title :
International Workshop on Machine Learning for Signal Processing (MLSP 2015)
City :
Boston
Country :
Etats-Unis d'Amérique
Start date of the conference :
2015-09-17
Journal title :
Proceedings of the International Workshop on Machine Learning for Signal Processing
Publication date :
2015
English keyword(s) :
Index Terms— Electronic support
super heterodyne
sensor scheduling
predictive state representation
subspace identification
HAL domain(s) :
Informatique [cs]/Apprentissage [cs.LG]
English abstract : [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 ...
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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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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