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Learning of scanning strategies for electronic support using predictive state representations
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.
Cited literature [27 references]
https://hal.inria.fr/hal-01225807
Contributor : Olivier Pietquin <>
Submitted on : Tuesday, November 10, 2015 - 10:40:24 PM
Last modification on : Friday, December 11, 2020 - 6:44:05 PM
Long-term archiving on: : Friday, February 12, 2016 - 5:08:54 PM
Contributor : Olivier Pietquin <>
Submitted on : Tuesday, November 10, 2015 - 10:40:24 PM
Last modification on : Friday, December 11, 2020 - 6:44:05 PM
Long-term archiving on: : Friday, February 12, 2016 - 5:08:54 PM
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MLSP_2015_HGCEJFGOP.pdf
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