Abstract
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.
Highlights
Synthetic Aperture Interferometric Radiometer (SAIR) technology represents a promising new approach to passive millimeter wave imaging for high-resolution observations of the target scene, both in near field and far field, which can be applied to areas such as indoor security, aircraft navigation, environment monitoring, atmosphere monitoring and so on [1,2,3]
The Compressive Sensing (CS) approach achieves high-resolution with very limited correlation observations and its advantages are of great practical use because the signal-to-noise of SAIR images is improved by reducing the multiplicative errors in correlation observations [7]
The basic CS approach adopts non-adaptive random measurement matrix Φ and constant basis matrix calculated by discrete cosine transform (DCT) transform for SAIR images, and correlation observations of SAIR are nondirective and redundant, different features of the unknown scene can be enhanced or weakened which may yield outliers in recovery, so the L1-minimization solution obtained by the basic
Summary
Synthetic Aperture Interferometric Radiometer (SAIR) technology represents a promising new approach to passive millimeter wave imaging for high-resolution observations of the target scene, both in near field and far field, which can be applied to areas such as indoor security, aircraft navigation, environment monitoring, atmosphere monitoring and so on [1,2,3]. To reduce the amount of data processing and achieve high-resolution, Compressive Sensing (CS) theory [4] is applied to SAIR based on the assumption that SAIR images can be sparsely represented in some spaces [5,6]. The CS approach achieves high-resolution with very limited correlation observations and its advantages are of great practical use because the signal-to-noise of SAIR images is improved by reducing the multiplicative errors in correlation observations [7]. Outliers are often present because the basic CS approach adopts a non-adaptive random measurement matrix, and the constant basis matrix for SAIR images and correlation observations of SAIR are nondirective and redundant [9,10]. A robust reweighted L1-minimization imaging algorithm, called RRIA, is introduced to passive millimeter wave SAIR images in near field.
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