This paper analyzes the potential for improvement in the performance of automatic target
recognition (ATR) for synthetic-aperture radar (SAR) with bistatic aspect diversity. Initial …

We extend the concept of monostatic aspect diversity for improved automatic target
recognition (ATR) to a bistatic synthetic aperture radar (SAR) platform. We derive the …

In this paper, a 2-D classifier using radial basis function neural networks (RBFNNs) has
been implemented combining two imageries collected by different locations to prove the …

Classification of targets in high-resolution synthetic aperture radar imagery is a challenging
problem in practice, due to extended operating conditions such as obscuration, articulation …

This paper continues the study reported in Ref. 1 and Ref. 2 trading off the fundamental ATR
performance capability (ie, algorithm-independent) of various SAR design options. The …

Multi‐feature decision fusion is a robust way to enhance the performance of synthetic
aperture radar (SAR) automatic target recognition (ATR). This study fuses two physically …

Similarity between model targets plays a fundamental role in determining the performance of
target recognition. We analyze the effect of model similarity on the performance of a vote …

A reliable target feature extraction process is proposed in this paper. The locations of
dominant scatterers have been widely adopted as target features in automatic target …

In this study, the authors introduce a novel method of target feature extraction that in one
form attempts to reduce variability inherent in target signatures and in a second form …

Two fusion strategies for target recognition using multi-aspect synthetic aperture radar (SAR)
images are presented for recognizing ground vehicles in MSTAR database. Due to radar …