Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
Artificial neural networks and deep learning techniques applied to radar target detection: A review
W Jiang, Y Ren, Y Liu, J Leng - Electronics, 2022 - mdpi.com
Radar target detection (RTD) is a fundamental but important process of the radar system,
which is designed to differentiate and measure targets from a complex background. Deep …
which is designed to differentiate and measure targets from a complex background. Deep …
Radar target characterization and deep learning in radar automatic target recognition: A review
W Jiang, Y Wang, Y Li, Y Lin, W Shen - Remote Sensing, 2023 - mdpi.com
Radar automatic target recognition (RATR) technology is fundamental but complicated
system engineering that combines sensor, target, environment, and signal processing …
system engineering that combines sensor, target, environment, and signal processing …
The rise of radar for autonomous vehicles: Signal processing solutions and future research directions
Automotive radar is the most promising and fastest-growing civilian application of radar
technology. Vehicular radars provide the key enabling technology for the autonomous …
technology. Vehicular radars provide the key enabling technology for the autonomous …
Deep-learning for radar: A survey
Z Geng, H Yan, J Zhang, D Zhu - IEEE Access, 2021 - ieeexplore.ieee.org
A comprehensive and well-structured review on the application of deep learning (DL) based
algorithms, such as convolutional neural networks (CNN) and long-short term memory …
algorithms, such as convolutional neural networks (CNN) and long-short term memory …
A study on radar target detection based on deep neural networks
L Wang, J Tang, Q Liao - IEEE Sensors Letters, 2019 - ieeexplore.ieee.org
Target detection is one of the most important radar applications widely used in practice.
Target detection can be regarded as a kind of classification, which distinguishes whether the …
Target detection can be regarded as a kind of classification, which distinguishes whether the …
Human motion recognition exploiting radar with stacked recurrent neural network
We develop a novel radar-based human motion recognition technique that exploits the
temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with …
temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with …
New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis
ABSTRACT Synthetic Aperture Radar (SAR) images are prone to be contaminated by noise,
which makes it very difficult to perform target recognition in SAR images. Inspired by great …
which makes it very difficult to perform target recognition in SAR images. Inspired by great …
Cognitive radar antenna selection via deep learning
Direction‐of‐arrival (DoA) estimation of targets improves with the number of elements
employed by a phased array radar antenna. Since larger arrays have high associated cost …
employed by a phased array radar antenna. Since larger arrays have high associated cost …
Video SAR moving target indication using deep neural network
J Ding, L Wen, C Zhong… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …
tracking of slow moving targets and for observing changes over short time periods. Shadows …