1 May 2000 Synthetic aperture radar automatic target recognition with three strategies of learning and representation
Author Affiliations +
Optical Engineering, 39(5), (2000). doi:10.1117/1.602495
Abstract
We describe a new architecture for synthetic aperture radar (SAR) automatic target recognition (ATR) based on the premise that the pose of the target is estimated within a high degree of precision. The advantage of our classifier design is that the input space complexity is decreased with the pose information, which enables fewer features to classify targets with a higher degree of accuracy. Moreover, the training of the classifier can be done discriminantly, which also improves performance and decreases the complexity of the classifier. Three strategies of learning and representation to build the pattern space and discriminant functions are compared: Vapnik's support vector machine (SVM), a newly developed quadratic mutual information (QMI) cost function for neural networks, and a principal component analysis extended recently with multiresolution (PCA-M). Experimental results obtained in the MSTAR database show that the performance of our classifiers is better than that of standard template matching in the same dataset. We also rate the quality of the classifiers for detection using confusers, and show significant improvement in rejection.
Qun Zhao, Jose C. Principe, Victor Brennan, Dongxin Xu, Zheng Wang, "Synthetic aperture radar automatic target recognition with three strategies of learning and representation," Optical Engineering 39(5), (1 May 2000). http://dx.doi.org/10.1117/1.602495
JOURNAL ARTICLE
15 PAGES


SHARE
KEYWORDS
Automatic target recognition

Synthetic aperture radar

Optical engineering

Principal component analysis

Tin

Target detection

Data storage

RELATED CONTENT


Back to Top