Integrating an automatic target recognition (ATR) system into real-world applications presents a challenge as it may frequently encounter new samples from unseen classes. To overcome this challenge, is necessary to adopt incremental learning, which enables the continuous acquisition of knowledge while retaining previous knowledge. This paper introduces novel, multi-purpose interpretability metr...

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of trainin...

The effectiveness of using the simulated synthetic aperture radar (SAR) images military targets in databases for automatic target recognition (ATR) is widely known. However, to be useful, they must sufficiently similar measured images; otherwise, can degrade ATR performance. Two factors affect quality SAR images: precision associated computer-aided design (CAD) model and accuracy speed numerica...

Enhancing target recognition from Synthetic Aperture Radar (SAR) images is a challenging task that cannot be generally solved through a unique and specific sensor configuration or signal processing solution. In particular, solutions exploiting physical target modelling not always are able to deal with complex targets or with small differences between classes. This issue can be solved if image p...

Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior ...

A novel multiview inverse synthetic aperture radar (ISAR) imaging method is proposed to simulate high-resolution and identifiable ISAR image of complex targets by handling large-angle wide-bandwidth scattering data. The data are simulated with the shooting bouncing ray (SBR) method. bidirectional ray-tracing algorithm developed reduce computation time. Simulation results indicate that improved ...

Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are particularly attractive since they allow receivers to operate without emitting energy, rendering them covert. Many existing passive radar systems estimate the locations and velocities of targets. This paper focuses on adding an autom...

ATR system has a broad application prospect in military, especially in the field of modern defense technology. When paradoxes are existence in ATR system due to adverse battlefield environment, integration cannot be effectively and reliably carried out only by traditional DS evidence theory. In this paper, A modified DS evidence theory is presented and applied in IR/MMW target recognition syste...

Automatic target recognition (ATR) algorithms are used to classify a given synthetic aperture radar (SAR) image into one of the known classes by using information gleaned from set training images that available for each class. Recently, deep learning methods have been shown achieve state-of-the-art classification accuracy if abundant data available, especially they sampled uniformly over and in...