Learning of scanning strategies for electronic support using predictive state representations

Hadrien Glaude 1, 2, 3 Cyrille Enderli 3 Jean-François Grandin 3 Olivier Pietquin 1, 4, 5, 2
1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
3 Thales Airborne Systems
4 Université de Lille, Sciences et Technologies
5 IUF - Institut Universitaire de France
Abstract : 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.
Keywords : subspace identification predictive state representation sensor scheduling Index Terms— Electronic support super heterodyne
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Conference papers
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Submitted on : Tuesday, November 10, 2015 - 10:40:24 PM
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Hadrien Glaude, Cyrille Enderli, Jean-François Grandin, Olivier Pietquin. Learning of scanning strategies for electronic support using predictive state representations. International Workshop on Machine Learning for Signal Processing (MLSP 2015), Sep 2015, Boston, United States. ⟨hal-01225807⟩

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