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

• Published in: IET radar, sonar & navigation Vol. 14; no. 7; pp. 1098 - 1104
• Main Authors: Guo, Jianwen, Shi, Hongyin, Yang, Ting, Qiao, Zhijun, Zhang, Jing
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.07.2020
• Subjects: CNN‐based network; convolutional neural network; echo; feature extraction; ground‐based P‐band radar echo signal; high‐quality image; image reconstruction; image resolution; image super‐resolution reconstruction; ionosphere disturbances; ionospheric disturbances; ionospheric effect correction; ionospheric total electron content; ISAR imaging method; phase error coarse compensation; phase error fine compensation; polar formatting algorithm; P‐band inverse synthetic aperture radar systems; P‐band ISAR imaging; radar imaging; Research Article; synthetic aperture radar; trained U‐net; ISAR imaging method; trained U-net; high-quality image; phase error fine compensation; polar formatting algorithm; convolutional neural network; ground-based P-band radar echo signal; echo; ionospheric effect correction; image reconstruction; phase error coarse compensation; ionospheric disturbances; image super-resolution reconstruction; synthetic aperture radar; ionospheric total electron content; P-band inverse synthetic aperture radar systems; ionosphere disturbances; P-band ISAR imaging; feature extraction; radar imaging; CNN-based network; image resolution
• ISSN: 1751-8784; 1751-8792; 1751-8792
• DOI: 10.1049/iet-rsn.2019.0625

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.