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On the application of neural networks to the classification of phase modulated waveforms
Author(s): Anthony Buchenroth; Joong Gon Yim; Michael Nowak; Vasu Chakravarthy
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Paper 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.

Paper Details

Date Published: 25 April 2017
PDF: 7 pages
Proc. SPIE 10205, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2017, 102050I (25 April 2017); doi: 10.1117/12.2264459
Show Author Affiliations
Anthony Buchenroth, Air Force Research Lab. (United States)
Joong Gon Yim, Booz Allen Hamilton (United States)
Michael Nowak, Air Force Research Lab. (United States)
Vasu Chakravarthy, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10205:
Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2017
Raja Suresh, Editor(s)

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