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• W4386760226 abstract "In low-frequency ultra-wideband (LFW) radar, the reconstruction of high-resolution range profile (HRRP) is an important task. The state-of-the-art compressive sensing (CS) method for HRRP reconstruction based on geometrical theory of diffraction (GTD) requires manual algorithm parameter tuning and is computationally expensive. In this paper, we propose a deep learning-based CS method for LFW radar HRRP reconstruction. We design a neural network architecture, i.e., target enhancement-based FISTA-Net (TEFISTA-Net), by unrolling the fast iterative shrinkage thresholding algorithm (FISTA). A new loss function based on the target-to-background ratio (TBR) is introduced for network training with the target enhancement capability in low signal-to-noise ratio (SNR) scenarios. The algorithm parameters in the traditional CS methods are substituted by network parameters learned from training data, getting rid of manual parameter tuning. In addition, simple convolution operations in our new method lead to lower computational complexity compared with existing methods. Experimental results based on diverse data show that the proposed method leads to higher computational efficiency with similar or better performance in comparison with existing state-of-the-art methods." @default.
• W4386760226 created "2023-09-16" @default.
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• W4386760226 date "2024-01-01" @default.
• W4386760226 modified "2023-09-29" @default.
• W4386760226 title "TEFISTA-Net: a learnable method for high-resolution range profile reconstruction with low-frequency ultra-wideband radar" @default.
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• W4386760226 doi "https://doi.org/10.1016/j.sigpro.2023.109257" @default.
• W4386760226 hasPublicationYear "2024" @default.
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