In recent years, convolutional neural networks (CNNs) have drawn considerable attention
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …

Nowadays, deep learning is a current and a stimulating field of machine learning. Deep
learning is the most effective, supervised, time and cost efficient machine learning approach …

Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture
with greater angular resolution than is inherently possible from the physical dimensions of a …

该文从新体制被动雷达的功能和性能优势出发, 首先简要回顾了被动雷达长达80
余年的研究历程; 然后较为全面地介绍了相关关键技术的研究进展, 包括参考信号重构 …

Video synthetic aperture radar (SAR) has been found to be very valuable for detection and
tracking of slow moving targets and for observing changes over short time periods. Shadows …

We propose a multimodal and multidiscipline data fusion strategy appropriate for automatic
target recognition (ATR) on synthetic aperture radar imagery. Our architecture fuses a …

Artificial Intelligence (AI) is increasingly finding acceptance in the space community,
especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous …

Given the functions and performance advantages of passive radar, this paper first reviews
the research history of passive radar for more than 80 years and then examines the research …

Compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods are
showing superior potential in imaging performance over classical matched filtering-based …

With the increasing complexity of the electromagnetic environment and continuous
development of radar technology we can expect a large number of modern radars using …