Abstract
Narrow-band Electronic Support receivers cannot detect radar signals in broad frequency ranges of the electromagnetic spectrum simultaneously. Hence, a frequency spectrum scanning strategy has to be planned. Commonly, this strategy is determined based on prior knowledge about possible threats. However, in an environment where the parameters of the radars are unconfirmed, it could be planned via learning-based representations. In previous researches, this sensor scheduling problem was modeled as a dynamical system by Predictive State Representations. Moreover, Singular Value Thresholding (SVT) algorithm is used in the subspace identification part to cope with the complexity of the system. In this work, We propose a scanning strategy learning method based on Robust Principal Component Analysis (RPCA).
Original language | English |
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Title of host publication | Artificial Intelligence and Machine Learning in Defense Applications III |
Editors | Judith Dijk |
Publisher | SPIE |
ISBN (Electronic) | 9781510645844 |
DOIs | |
Publication status | Published - 2021 |
Event | Artificial Intelligence and Machine Learning in Defense Applications III 2021 - Virtual, Online, Spain Duration: 13 Sept 2021 → 17 Sept 2021 |
Publication series
Name | Proceedings of SPIE - The International Society for Optical Engineering |
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Volume | 11870 |
ISSN (Print) | 0277-786X |
ISSN (Electronic) | 1996-756X |
Conference
Conference | Artificial Intelligence and Machine Learning in Defense Applications III 2021 |
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Country/Territory | Spain |
City | Virtual, Online |
Period | 13/09/21 → 17/09/21 |
Bibliographical note
Publisher Copyright:© 2021 SPIE
Keywords
- Electronic Support Measures
- Electronic Support Receiver
- Electronic Warfare
- Frequency Search Strategy
- Robust Principal Component Analysis (RPCA)
- Scanning Regime Learning