Obtaining measured synthetic aperture radar (SAR) data for training automatic target
recognition (ATR) models can be too expensive (in terms of time and money) and complex …

While many synthetic aperture radar (SAR) image formation techniques exist, two of the
most intuitive methods for implementation by SAR novices are the matched filter and …

Over the past decades, there have been many approaches to synthetic aperture radar (SAR)
automatic target recognition (ATR). ATR includes detection, classification, and identification …

Mass production of high-quality synthetic SAR training imagery is essential for boosting the
performance of deep-learning (DL)-based SAR automatic target recognition (ATR) …

Abstract Circular Synthetic Aperture Radar (CSAR) is a novel imaging mode, which has the
advantages of all-directional observation, high spatial resolution, and three-dimensional …

Training sets for supervised classification tasks are usually limited in scope and only contain
examples of a few classes. In practice, classes that were not seen in training are given …

Training deep learning-based synthetic aperture radar automatic target recognition (SAR-
ATR) systems for use in an “open-world” operating environment has, thus far proven difficult …

In real-world scenarios, it may not always be possible to collect hundreds of labeled
samples per class for training deep-learning-based synthetic aperture radar (SAR) …

We propose a novel framework for the deterministic construction of linear, near-isometric
embeddings of a finite set of data points. Given a set of training points X⊂\BBR N, we …

The target recognition system based on radar sensor network (RSN) has recently been
widely studied in radar automatic target recognition (RATR). The system can observe the …