Results 21 to 30 of about 1,336 (197)

Georadar Waveform Characterization of Tunnel Lining Rear Defects and Joint Detection Method in Time and Frequency Domains [PDF]

Sensors
Aiming at the signal interference and feature recognition difficulties existing in the detection of concealed defects such as cracks and voids behind the tunnel lining, this study carried out a 1:1 reinforced concrete–steel arch frame composite lining ...
Jian Liu, Wei Yan, Gaohang Lv, Lei Kou, Bo Li, Xiao Zhang, Guanhong Lu, Quanyi Xie   +7 more
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Machine Learning For Modulation Classification Of Radar Signals: A Survey

JISR on Computing, 2019
Automatic modulation recognition of radar waveform is a major topic and has many military applications. This paper surveys the models and the techniques used in recognizing different modulation types of intercepted radar waveform.
Hitham Alshoubaki
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Adaptive Multi-Function Radar Temporal Behavior Analysis

Remote Sensing
The performance of radar mode recognition has been significantly enhanced by the various architectures of deep learning networks. However, these approaches often rely on supervised learning and are susceptible to overfitting on the same dataset.
Zhenjia Xu, Qingsong Zhou, Zhihui Li, Jialong Qian, Yi Ding, Qinxian Chen, Qiyun Xu   +6 more
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A New Waveform Design Method for Multi-Target Inverse Synthetic Aperture Radar Imaging Based on Orthogonal Frequency Division Multiplexing Chirp

Remote Sensing
With the increasing use of the strategy and group target attack method in the modern battlefield, multi-target inverse synthetic aperture radar (ISAR) imaging simultaneously with high efficiency draws more and more attention, which gives a promising ...
Xuebo Zou, Guanghu Jin, Feng He, Yongsheng Zhang   +3 more
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Radar Constant-Modulus Waveform Optimization for High-Resolution Range Profiling of Stationary Targets

Sensors, 2017
The high-resolution range (HRR) profile is an important target signature in many applications (e.g., automatic target recognition), and the radar HRR profiling performance is highly dependent on radar transmitted waveforms. In this paper, we consider the
Wenzhen Yue, Lin Li, Yu Xin, Tao Han
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Narrowband Radar Micromotion Targets Recognition Strategy Based on Graph Fusion Network Constructed by Cross-Modal Attention Mechanism

Remote Sensing
In the domain of micromotion target recognition, target characteristics can be extracted through either broadband or narrowband radar echoes. However, due to technical limitations and cost constraints in acquiring broadband radar waveform data ...
Yuanjie Zhang, Ting Gao, Hongtu Xie, Haozong Liu, Mengfan Ge, Bin Xu, Nannan Zhu, Zheng Lu   +7 more
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Deep-Learning for Radar: A Survey

IEEE Access, 2021
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 (LSTM), in radar signal processing is given.
Zhe Geng, He Yan, Jindong Zhang, Daiyin Zhu   +3 more
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Radar waveform design and processing using joint amplitude‐frequency‐phase modulation

IET Radar, Sonar & Navigation, 2023
Radar waveform design is facing severe challenges with the development of radar countermeasure technology, such as easy access to intra‐pulse modulation property and poor anti‐recognition capabilities.
Tiehua Zhao, Qihua Wu, Feng Zhao, Xiaobin Liu, Shunping Xiao   +4 more
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Densely-Accumulated Convolutional Network for Accurate LPI Radar Waveform Recognition [PDF]

, 2022
This paper presents a deep learning-based method to automatically recognize low probability of intercept (LPI) radar waveforms against diversified jamming attacks.
Kim, Dong-Seong, Nguyen, Toan-Van, Nguyen, Nhan Thanh, Huynh-The, Thien, Pham, Quoc-Viet, Doan, Van-Sang, Benevides da Costa, Daniel   +6 more
core   +1 more source

Recognition of Radar Emitters with Agile Waveform Based on Hybrid Deep Neural Network and Attention Mechanism [PDF]

Radioengineering, 2021
With the increasing complexity of the electromagnetic environment and the continuous development of radar technology, more and more modern digital programmable radars using agile waveform will appear in the future battlefield.
Y. Feng, G. Wang, Z. Liu, B. Cui, Y. Yang, X. Xu, H. Han   +6 more
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