Qian, J., Huang, S., Wang, L., Bi, G., & Yang, X. (2021). Super-Resolution ISAR Imaging for Maneuvering Target Based on Deep-Learning-Assisted Time-Frequency Analysis. IEEE Transactions on Geoscience and Remote Sensing, 1–14. doi:10.1109/tgrs.2021.3050189
Abstract:
Traditional range-instantaneous Doppler (RID) methods for maneuvering target imaging suffer from the problems of low resolution and poor noise suppression. We propose a new super-resolution inverse synthetic aperture radar (ISAR) imaging method based on deep-learning-assisted time-frequency analysis (TFA). Our deep neural network resembles the basic structure of a U-net with two additional convolutional-upsampling layers and l₁-norm loss function for super-resolution generation and noise suppression. The neural network is trained in advance to learn the mapping function between the low-resolution time-frequency spectrum inputs and their high-resolution references. Then, the linear TFA assisted by the pretrained network is integrated into the RID-based ISAR imaging system and is found to achieve sharply focused and denoised target image with super-resolution. Both the simulated and real radar data are used to evaluate the performance of the proposed method. Numerical experimental results demonstrate the superiority of the proposed ISAR imaging method over traditional ones.
License type:
Publisher Copyright
Funding Info:
This work was supported in part by the National Natural Science Foundation of China under Grant 61401077 and Grant 61501375, in part by the Sichuan Science and Technology Program under Grant 2019YFG0099, in part by the China Postdoctoral Science Foundation under Grant 2015M580784, and in part by the the Natural Science
Basis Research Plan in Shaanxi Province of China under Grant 2018JM6020.