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 ...
Show more >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