[HTML][HTML] Radar emitter multi-label recognition based on residual network
Y Hong-hai, Y Xiao-peng, L Shao-kun, L Ping… - Defence …, 2022 - Elsevier
In low signal-to-noise ratio (SNR) environments, the traditional radar emitter recognition
(RER) method struggles to recognize multiple radar emitter signals in parallel. This paper …
(RER) method struggles to recognize multiple radar emitter signals in parallel. This paper …
Toward next-generation signal intelligence: A hybrid knowledge and data-driven deep learning framework for radio signal classification
S Zheng, X Zhou, L Zhang, P Qi, K Qiu… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Automatic modulation classification (AMC) can generally be divided into knowledge-based
methods and data-driven methods. In this paper, we explore combining the knowledge …
methods and data-driven methods. In this paper, we explore combining the knowledge …
Automatic modulation recognition of compound signals using a deep multi-label classifier: A case study with radar jamming signals
The modern battlefield is getting more complicated due to the increasing number of different
radiation sources as well as their fierce contention (interference) and confrontations …
radiation sources as well as their fierce contention (interference) and confrontations …
Automatic modulation classification using compressive convolutional neural network
The deep convolutional neural network has strong representative ability, which can learn
latent information repeatedly from signal samples and improve the accuracy of automatic …
latent information repeatedly from signal samples and improve the accuracy of automatic …
3D convolutional neural networks based automatic modulation classification in the presence of channel noise
Automatic modulation classification is a task that is essentially required in many intelligent
communication systems such as fibre‐optic, next‐generation 5G or 6G systems, cognitive …
communication systems such as fibre‐optic, next‐generation 5G or 6G systems, cognitive …
Automatic waveform recognition of overlapping LPI radar signals based on multi-instance multi-label learning
In an ever-increasingly complex electromagnetic environment, multiple low probability of
intercept (LPI) radar emitters may transmit their own signals simultaneously on similar …
intercept (LPI) radar emitters may transmit their own signals simultaneously on similar …
Residual attention-aided U-Net GAN and multi-instance multilabel classifier for automatic waveform recognition of overlapping LPI radar signals
Z Pan, S Wang, Y Li - IEEE Transactions on Aerospace and …, 2022 - ieeexplore.ieee.org
Automatic waveform recognition of overlapping low probability of intercept (LPI) radar
signals is an important and challenging task in electronic reconnaissance of the increasingly …
signals is an important and challenging task in electronic reconnaissance of the increasingly …
Semantic learning for analysis of overlapping LPI radar signals
The increasingly complex radio environment may cause the received low probability of
intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping …
intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping …
Recognition and Estimation for Frequency-Modulated Continuous-Wave Radars in Unknown and Complex Spectrum Environments
K Chen, J Zhang, S Chen, S Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar
signals are critical for both military electronic countermeasures and civilian autonomous …
signals are critical for both military electronic countermeasures and civilian autonomous …
Cross model deep learning scheme for automatic modulation classification
Deep Neural Networks (DNNs) have achieved remarkable accuracy improvements for
automatic modulation classification. However, the employed networks often have millions of …
automatic modulation classification. However, the employed networks often have millions of …