研究目的
To recover missing spectral information in UWB SAR imaging caused by RF spectral restrictions, enabling high-quality imaging and target discrimination in spectrally congested environments.
研究成果
The proposed sparsity-driven spectral recovery framework effectively reconstructs missing spectral data, improving SAR image quality and discrimination performance, with gains up to 46 dB in some cases, and maintains high accuracy even with up to 70% missing bandwidth.
研究不足
The method assumes sparsity of radar signals, which may not hold for non-sparse scenes. Computational complexity increases with algorithms like ADMM. Real-world RF interference and hardware limitations are not addressed.
1：Experimental Design and Method Selection:
The study uses a compressed sensing framework to model the problem, with sparse recovery algorithms (e.g., OMP, ADMM) applied to stepped-frequency radar data.
2：Sample Selection and Data Sources:
EM simulations using FDTD software generate data for targets (e.g., metal mines, UXO) and clutter objects (e.g., soda can, rocks) at all aspect angles from 0° to 360°.
3：0°. List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: No specific physical equipment is mentioned; simulations are conducted using computational methods.
4：Experimental Procedures and Operational Workflow:
Radar signals are simulated with missing frequency bands; sparse recovery is applied to reconstruct full-spectrum data, followed by SAR image formation using back-projection.
5：Data Analysis Methods:
Performance is evaluated using processing gain metrics and discrimination accuracy between targets and clutter.
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