- 标题
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Modular Subarrayed Phased Array Design by Means of Iterative Convex Relaxation Optimization
摘要： Subarray design technique is widely employed in the design of planar phased arrays. However, it’s hard to obtain full coverage and good radiation performance with the given shape of subarrays and array aperture. In this letter, the subarray design problem is formulated as an iterative convex relaxation model and solved through some steps subtly. Besides, the scanning sidelobe level is adopted to retrieve the optimal configuration among the found solutions. Numerical experiments are carried out to assess the performance of the proposed method as well as the inherent superiority in adaptive beamforming.
关键词： modular subarray,convex relaxation,subarray design,adaptive beamforming
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Non-Convex Low-Rank Approximation for Hyperspectral Image Recovery with Weighted Total Varaition Regularization
摘要： Low-rank representation has been widely used as a powerful tool in hyperspectral image (HSI) recovery. The existing studies involving low-rank problems are commonly under the nuclear norm penalization. However, nuclear norm minimization tends to over-shrink the components of rank, which leads to modeling bias. In this paper, a new non-convex penalty is introduced to obtain an unbiased low-rank approximation. In Addition, local spatial neighborhood weighted spectral-spatial total variation (TV) regularization is introduced to preserve spatial structural information. And sparse 1l-norm is used as a constraint to sparse noise. Finally, a novel HSI non-convex low-rank relaxation restoration model is proposed. A number of experiments show that the proposed method can effectively remove the mixed-noise, and result in an unbiased estimate with better robustness.
关键词： Hyperspectral image(HSI),total variation(TV),low-rank representation,non-convex relaxation
更新于2025-09-10 09:29:36