On the application of neural networks to the classification of phase modulated waveforms
| Autor: | Vasu Chakravarthy, Anthony Buchenroth, Joong Gon Yim, Michael J. Nowak |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Convolutional neural network law.invention law 0202 electrical engineering electronic engineering information engineering Waveform Artificial intelligence Radar business Classifier (UML) |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2264459 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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