NDLI: Ionospheric correction in P-band ISAR imaging based on polar formatting algorithm and convolutional neural network

Content Provider	IET Digital Library
Author	Guo, Jianwen Shi, Hongyin Yang, Ting Qiao, Zhijun Zhang, Jing
Abstract	The ionosphere causes serious phase error in P-band inverse synthetic aperture radar (ISAR) systems, which makes it difficult to obtain a high-quality image. Recently, the convolutional neural network (CNN) has gained much attention in signal processing, and it can automatically extract features to realise an image super-resolution reconstruction. As a popular CNN-based network, U-net can work with less training samples. Hence, the authors are interested in exploiting and modifying the U-net to enhance the P-band ISAR imaging. In this study, in light of the analysis of the effect of the ionospheric total electron content on the ground-based P-band radar echo signal, a novel ISAR imaging method is proposed for the ionospheric effect correction based on the modified U-net and polar formatting algorithm (PFA). The PFA is performed for the phase error coarse compensation. Then, the phase error fine compensation is exploited by the trained U-net. The proposed method can adapt the ionosphere disturbances and show high performance in imaging quality and computational efficiency. The simulation results show the effectiveness of the proposed method.
Starting Page	1098
Ending Page	1104
Page Count	7
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 7, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0625
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-03-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CNN-based Network Computer Vision And Image Processing Technique Convolutional Neural Network Echo Electromagnetic Compatibility Feature Extraction Ground-based P-band Radar Echo Signal High-quality Image Image Processing Image Recognition Image Reconstruction Image Resolution Image Super-resolution Reconstruction Interference Ionosphere Disturbances Ionospheric Disturbances Ionospheric Disturbances And Modification Experiments Ionospheric Effect Correction Ionospheric Total Electron Content ISAR Imaging Method Neural Computing Technique P-band Inverse Synthetic Aperture Radar System P-band ISAR Imaging Phase Error Coarse Compensation Phase Error Fine Compensation Polar Formatting Algorithm Radar Equipment Radar Imaging Restoration Synthetic Aperture Radar System And Application Trained U-net
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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