تقرير
Using a Kernel Adatron for Object Classification with RCS Data
| العنوان: | Using a Kernel Adatron for Object Classification with RCS Data |
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| المؤلفون: | Byl, Marten F., Demers, James T., Rietman, Edward A. |
| سنة النشر: | 2010 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Learning, Statistics - Machine Learning |
| الوصف: | Rapid identification of object from radar cross section (RCS) signals is important for many space and military applications. This identification is a problem in pattern recognition which either neural networks or support vector machines should prove to be high-speed. Bayesian networks would also provide value but require significant preprocessing of the signals. In this paper, we describe the use of a support vector machine for object identification from synthesized RCS data. Our best results are from data fusion of X-band and S-band signals, where we obtained 99.4%, 95.3%, 100% and 95.6% correct identification for cylinders, frusta, spheres, and polygons, respectively. We also compare our results with a Bayesian approach and show that the SVM is three orders of magnitude faster, as measured by the number of floating point operations. Comment: This material is based upon work supported by US Army Space & Missile Command under Contract Number W9113M-07-C-0204. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily re flect the views of US Army Space & Missile Command |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1005.5337 |
| رقم الأكسشن: | edsarx.1005.5337 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |