A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field

Sensors

Yilong Zhang¹  Yuehua Li¹  Shujin Zhu¹  Yuanjiang Li¹ 

[1] School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; E-Mails:

关键词: passive millimeter wave;    SAIR;    reweighted L₁-minimization;    prior information;   

DOI  :  10.3390/s151024945

来源: mdpi

【 摘 要 】

The Compressive Sensing (CS) approach has proven to be useful for Synthetic Aperture Interferometric Radiometer (SAIR) imaging because it provides the same high-resolution capability while using part of interferometric observations compared to traditional methods using the entirety. However, it cannot always obtain the sparsest solution and may yield outliers with the non-adaptive random measurement matrix adopted by current CS models. To solve those problems, this paper proposes a robust reweighted L₁-minimization imaging algorithm, called RRIA, to reconstruct images accurately by combining the sparsity and prior information of SAIR images in near field. RRIA employs iterative reweighted L₁-minimization to enhance the sparsity to reconstruct SAIR images by computing a new weight factor in each iteration according to the previous SAIR images. Prior information estimated by the energy functional of SAIR images is introduced to RRIA as an additional constraint condition to make the algorithm more robust for different complex scenes. Compared to the current basic CS approach, our simulation results indicate that RRIA can achieve better recovery with the same amount of interferometric observations. Experimental results of different scenes demonstrate the validity and robustness of RRIA.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.