Scanning strategy learning for electronic support receivers by robust principal component analysis

İsmail Gül; Isin Erer

doi:10.1117/12.2601109

12 September 2021 Scanning strategy learning for electronic support receivers by robust principal component analysis

İsmail Gül, Isin Erer

Proceedings Volume 11870, Artificial Intelligence and Machine Learning in Defense Applications III; 118700Q (2021) https://doi.org/10.1117/12.2601109
Event: SPIE Security + Defence, 2021, Online Only

Conference Poster

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).

Conference Presentation

Citation Download Citation

İsmail Gül and Isin Erer "Scanning strategy learning for electronic support receivers by robust principal component analysis", Proc. SPIE 11870, Artificial Intelligence and Machine Learning in Defense Applications III, 118700Q (12 September 2021); https://doi.org/10.1117/12.2601109

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