Amethod to automatically detect targets from sets of pixelregistered visual, thermal, and range images is outlined. It uses operations speci cally designed to work on the di erent kinds of images to explote the information given by each of them. Five features are used to distinguish the targets from the clutter: texture, brightness, temperature, surface planarity, and height. The results from i...

To reduce the complexity of classifier design in radar automatic target recognition (RATR), a novel RATR method for high range resolution profile (HRRP) is proposed. Linearly separable features are extracted with sequential vanishing component analysis (SVCA) which is implemented by finding the generators of each approximately vanishing polynomial set, and target classification is implemented w...

Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhoodbased approaches measure the degree that a candidate term refers to a domain specific concept. Unithood-based approaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using different, often incompatible evaluation sche...

Over the past several years, the military has grown increasingly reliant upon the use of unattended aerial vehicles (UAVs) for surveillance missions. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air. Such systems tend to be used primarily for the purpose of acquiring sensory data with the goal of automatic detection, identification...

Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks. To improve track quality, we extend previous tracking approaches by investigating human cognitive-level fusion for constraining the set of plausible targets where the number of targets is not known a priori. The target track algorithm predicts a belief in the position and pose for a ...

This paper describes an automatic target recognition system for detecting targets in temporal sequences of intensity LADAR images. The system rst nds all objects in the images using a general method that is capable of nding both blobs and curves. Then features of the objects are extracted. Next fuzzy c-means is used to cluster the objects in feature space. Finally, prototypes generated for each...

Energy is a critical performance metric in collaborative and distributed wireless networks. An integrated approach that considers system-level energy tradeoffs (such as communication versus computation energy) is essential for energy efficient application development in these networks. In this paper, we present a system-level model for estimating the energy dissipation in these networks, and di...

Following the recent advancements in radar technologies, research on automatic target recognition using Inverse Synthetic Aperture Radar (ISAR) has correspondingly seen more attention and activity. ISAR automatic target recognition researchers aim to fully automate recognition and classification of military vehicles, but because radar images often do not present a clear image of what they detec...

We present a parametric attributed scattering model for Synthetic Aperture Radar imagery The model characterizes both frequency and aspect dependence of scattering centers We present algorithms for estimating the model pa rameters from SAR image chips and propose model order estimation algorithms that exploit nested model structures We develop a Bayes classi er for the extracted model parameter...

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added targeted objects. This leads serious safety concerns applying DNNs high-stake SAR ATR applications. Therefore, enhancing the robustnes...