School of Mathematical & Statistical Sciences Faculty Publications and Presentations

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

Document Type

Article

Publication Date

7-2020

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.

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© 2020 The Institution of Engineering and Technology

Publication Title

IET Radar, Sonar & Navigation

DOI

10.1049/iet-rsn.2019.0625

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