25 April 2017 On the application of neural networks to the classification of phase modulated waveforms
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Abstract
Accurate classification of phase modulated radar waveforms is a well-known problem in spectrum sensing. Identification of such waveforms aids situational awareness enabling radar and communications spectrum sharing. While various feature extraction and engineering approaches have sought to address this problem, the use of a machine learning algorithm that best utilizes these features is becomes foremost. In this effort, a comparison of a standard shallow and a deep learning approach are explored. Experiments provide insights into classifier architecture, training procedure, and performance.
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Anthony Buchenroth, Anthony Buchenroth, Joong Gon Yim, Joong Gon Yim, Michael Nowak, Michael Nowak, Vasu Chakravarthy, Vasu Chakravarthy, } "On the application of neural networks to the classification of phase modulated waveforms", Proc. SPIE 10205, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2017, 102050I (25 April 2017); doi: 10.1117/12.2264459; https://doi.org/10.1117/12.2264459
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