| タイトル | Radar signal categorization using a neural network |
| 本文（外部サイト） | http://hdl.handle.net/2060/19910012469 |
| 著者（英） | Penz, P. Andrew; Gately, Michael T.; Anderson, James A.; Collins, Dean R. |
| 著者所属（英） | Brown Univ. |
| 発行日 | 1991-02-01 |
| 言語 | eng |
| 内容記述 | Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications. |
| NASA分類 | CYBERNETICS |
| レポートNO | 91N21782 |
| 権利 | No Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/133063 |
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