| Paper: | ITT-P1.7 | ||
| Session: | Defense and Security Applications | ||
| Time: | Wednesday, May 17, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Industry Technology Track: Decluttering Target Identification and Tracking | ||
| Title: | RADAR SIGNAL CLASSIFICATION USING PCA-BASED FEATURES | ||
| Authors: | Amit Mishra, Bernard Mulgrew, University of Edinburgh, United Kingdom | ||
| Abstract: | Principal component analysis (PCA) has been used in many applications (mainly) for the purpose of data compression and feature extraction. Usage of PCA for synthetic aperture radar (SAR) image classification, though widely reported in remote-sensing researchers, has not been exploited much by automatic target recognition (ATR) community. In the present paper, PCA has been used in SAR-ATR using the MSTAR data base, and comparison has been made with the conventional conditional Gaussian model based Bayesian (CGB) classifier. The results have been compared based on percentage of correct classification, receiver performance characteristics (ROC), and performance with limited amount of training data. By all standards of comparison, the PCA based classifier was observed to outperform the CGB classifier. And given the computational simplicity and fastness of PCA based classifier, the new algorithm was concluded to be a highly prospective candidate for real time ATR systems. | ||
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