In this paper we present a fuzzy system based hyperspectral classi®er for automatic target identi®cation. The system is based on partitioning the spectral band space into clusters using a modi®ed fuzzy C-Means clustering algorithm. Classi®cation of each pixel is then carried out by calculating its fuzzy membership in each cluster. The results showed that the fuzzy hyperspectral classi®er is suc...

In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic target recognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also pro...

This paper describes the implementation of an automatic target recognition (ATR) Probing algorithm on a recon gurable system, using the SA-C programming language and optimizing compiler. The recon gurable system is 800 times faster than a comparable Pentium running a C implementation of the same probing task. The reasons for this are analyzed.

Inverse synthetic aperture radar (ISAR) imaging is an effective method to identify unknown targets regardless of weather and illumination conditions. Research results published regarding this topic have focused mainly on imaging and automatic target recognition (ATR) of single targets. However, targets generally fly in formation, so the applicability of ISAR images to ATR of multiple targets mu...

Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR) automatic target recognition (ATR) for years. However, under limited SAR images, width and depth of CNN-based models are limited, widening received field global features in images is hindered, which finally leads to low performance recognition. To address these challenges, we propose a Transformer (ConvT) ATR...

Automatic target recognition (ATR) performance modeling is dependent on model complexity, training data, and test analysis . In order to compare different ATR algorithms, we develop a fidelity score that characterizes the quality of different algorithms to meet real-world conditions. For instance, a higher fidelity ATR performance model (PM) is robust over many operating conditions (sensors, ta...

Automatic target recognition (ATR) for military applications is one of the core processes toward enhancing intelligence and autonomously operating platforms. Spurred by this given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, article surveys assesses current SAR ATR algorithms employ most popular dataset domain, namely moving stationary acqui...

In this paper, a new 1-D hybrid Automatic Target Recognition (ATR) algorithm is developed for High Range Resolution (HRR) profiles. The proposed hybrid algorithm combines EigenTemplate based Matched Filtering (ETMF) and Hidden Markov modeling (HMM) techniques to achieve superior HRR-ATR performance. In the algorithm, each HRR test profile is first scored by ETMF which is then followed by indepe...