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11 January 2020 Inverse synthetic aperture radar imaging based on time–frequency analysis through neural network
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Abstract

Time–frequency analysis is a fundamental tool in many applications, such as inverse synthetic aperture radar (ISAR) imaging along the cross-range direction. Traditional time–frequency transformations designed for the general signal suffer low time–frequency resolution or cross-term interference. In this study, a cascaded UNet-like network is applied to the refinement of basic transformations, and another forward regression network is proposed to estimate the signal parameters directly. Both networks can incorporate a priori information and combine different time–frequency transformations efficiently. Several experiments, especially in the ISAR application, are presented to validate the methods. Through this research, the neural network is a promising approach to develop a customized method with high performance for a specific signal processing problem.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00 © 2020 SPIE and IS&T
Hang Chen, Junjun Yin, Chunmao Yeh, Yaobing Lu, and Jian Yang "Inverse synthetic aperture radar imaging based on time–frequency analysis through neural network," Journal of Electronic Imaging 29(1), 013003 (11 January 2020). https://doi.org/10.1117/1.JEI.29.1.013003
Received: 9 September 2019; Accepted: 18 December 2019; Published: 11 January 2020
JOURNAL ARTICLE
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