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• W4316661105 abstract "Compressed sensing (CS)-based imaging technology has attracted a lot of interest because it can enhance imaging resolution. Targets of interest for forward-looking imaging radar are typically few in comparison to the entire imaging region. This sparsity allows for the natural application of CS to the reconstruction of high-resolution forward-looking images. However, conventional CS-based imaging methods can only perform well when the signal-to-noise ratio (SNR) is high. Strong noise in radar imaging prevents the CS-based methods from producing excellent imaging results. Inspired by the low-rank property of the received radar target echo and the sparsity of the forward-looking image targets, we present a combined low-rank and sparse prior restricted model for forward-looking imaging with a multichannel array radar to overcome strong noise. To solve the low-rank joint sparse double prior constraint optimization problem, an augmented Lagrange multiplier (ALM) reconstruction method under the framework of the alternating direction multiplier method (ADMM) is proposed. Finally, the results of simulation and real measurement data indicate that our presented method is fairly effective at enhancing the azimuth resolution and robustness of forward-looking radar imaging in comparison to other current methods." @default.
• W4316661105 created "2023-01-17" @default.
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• W4316661105 date "2023-01-01" @default.
• W4316661105 modified "2023-09-29" @default.
• W4316661105 title "Enhancing Forward-Looking Image Resolution: Combining Low-Rank and Sparsity Priors" @default.
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• W4316661105 doi "https://doi.org/10.1109/tgrs.2023.3237332" @default.
• W4316661105 hasPublicationYear "2023" @default.
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