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.
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