Jiadian Liang

Orcid: 0000-0002-6121-3080

According to our database1, Jiadian Liang authored at least 14 papers between 2020 and 2022.

Collaborative distances:
  • Dijkstra number2 of six.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
AF-AMPNet: A Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing.
IEEE Trans. Geosci. Remote. Sens., 2022

RMIST-Net: Joint Range Migration and Sparse Reconstruction Network for 3-D mmW Imaging.
IEEE Trans. Geosci. Remote. Sens., 2022

Lightweight FISTA-Inspired Sparse Reconstruction Network for mmW 3-D Holography.
IEEE Trans. Geosci. Remote. Sens., 2022

Non-Line-of-Sight Imaging of Hidden Moving Target using Millimeter-wave Inverse Synthetic Aperture Radar.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

Image Enhancement of 3-D SAR via U-Net Framework.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
TPSSI-Net: Fast and Enhanced Two-Path Iterative Network for 3D SAR Sparse Imaging.
IEEE Trans. Image Process., 2021

Robust and Efficient ISAR Autofocusing Based on Deep Convolution Network.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

2020
JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals.
IEEE Trans. Veh. Technol., 2020

FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection.
Remote. Sens., 2020

CIST: An Improved ISAR Imaging Method Using Convolution Neural Network.
Remote. Sens., 2020

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery.
Remote. Sens., 2020

Efficient Insar Imaging Based on Frequency-Domain Back Projection Algorithm.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020

Linear Array 3-D SAR Sparse Imaging via Convolutional Neural Network.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020

ISAR Compressive Sensing Imaging Using Convolution Neural Network with Interpretable Optimization.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020


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