Joint Sparsity Constraint Interferometric ISAR Imaging for 3-D Geometry of Near-Field Targets with Sub-Apertures

Abstract

This paper proposes a new interferometric near-field 3-D imaging approach based on multi-channel joint sparse reconstruction to solve the problems of conventional methods, i.e., the irrespective correlation of different channels in single-channel independent imaging which may lead to deviated positions of scattering points, and the low accuracy of imaging azimuth angle for real anisotropic targets. Firstly, two full-apertures are divided into several sub-apertures by the same standard; secondly, the joint sparse metric function is constructed based on scattering characteristics of the target in multi-channel status, and the improved Orthogonal Matching Pursuit (OMP) method is used for imaging solving, so as to obtain high-precision 3-D image of each sub-aperture; thirdly, comprehensive sub-aperture processing is performed using all sub-aperture 3-D images to obtain the final 3-D images; finally, validity of the proposed approach is verified by using simulation electromagnetic data and data measured in the anechoic chamber. Experimental results show that, compared with traditional interferometric ISAR imaging approaches, the algorithm proposed in this paper is able to provide a higher accuracy in scattering center reconstruction, and can effectively maintain relative phase information of channels.

Direction of Arrival Estimation by Matching Pursuit Algorithm With Subspace Information

“…The theory notes that [1]- [3] if a signal is sparse or sparse after a certain transformation, the high-dimensional signal can be projected into a low-dimensional space and reconstructed from a small set of low-dimensional data with high probability. CS theory affects many research fields, including radar image processing [4]- [6], blind source separation [7]- [9], sensor networks [10], [11], and Internet of The associate editor coordinating the review of this manuscript and approving it for publication was Zilong Liu.…”

Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data

“…The joint sparsity for sparsity reconstruction is realized by incorporating sparsity constraints and structural constraints into the reconstruction imaging processing, which can contribute to a new sparsity scene according to the prior structural information that is excavated. Joint sparsity not only emphasizes the sparsity characteristics in traditional CS theory, but also improves its disadvantages of being greatly affected by noise and having pseudo-values [ 31 , 53 ]. Its principle is also derived from Equation (2): …”

Special Issue “Synthetic Aperture Radar (SAR) Techniques and Applications”

“…The special issue includes also three interesting papers [8][9][10] dedicated to ground-based SAR (GBSAR) systems.…”

“…Finally, the work in [10] proposes improving the interferometric near-field 3D imaging by using a multichannel joint sparse reconstruction. The basic idea consists in deriving multichannel signals by dividing the two observed full apertures into sub-apertures.…”