Two-dimensional reweighted alternating direction method of multipliers for inverse synthetic aperture radar imaging with pattern-coupled sparse structure

Xu, Wei; Yang, Jun; Lv, Mingjiu; Chen, Wenfeng; Xiao-yan, MA

doi:10.1117/1.jrs.15.044518

Cited by 2 publications

(2 citation statements)

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“…Next, under the conditions of noise and sparse aperture, the imaging performance of the main body after the removal of m-D effects is analyzed. Moreover, the imaging performance of different algorithms is measured by three indexes [19], such as image entropy (IE), image contrast (IC), and running time (Time). Among them, Time is described by the running time of the computer.…”

Section: Experimental Results Of Simulated Datamentioning

confidence: 99%

“…However, during the actual imaging process, the timesharing working mode of the radar, the noncooperation of the target, and strong noise interference will make the echo pulse limited or incomplete, namely, sparse aperture, leading to the high side and grating lobes in the RD imaging result [19], which may not only affect the imaging quality of the main body but also challenge the original m-D effects removal algorithms. In recent years, compressive sensing (CS) is widely applied to ISAR imaging and multiple-input multiple-output (MIMO) radar signal processing [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

Xu¹,

Yang²,

Lv³

et al. 2022

Mathematical Problems in Engineering

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For inverse synthetic aperture radar (ISAR) imaging of targets with micromotion parts, the removal of micro-Doppler (m-D) effects is the key procedure. However, under the condition of a sparse aperture, the echo pulse is limited or incomplete, giving rise to the difficulty of eliminating m-D effects. Thus, a novel m-D effects removal algorithm is proposed, which can effectively eliminate m-D effects, as well as the interference introduced by noise and sparse aperture in the ISAR image of the main body. The proposed algorithm mainly includes two processing steps. First, combined with the cut-off point determined by the normalized singular value difference spectrum, the rough estimation of the main body is achieved by singular value decomposition (SVD). Then, the variational mode extraction (VME) is extended to complex variational mode extraction (CVME). The constrained variational problem constructed by bandwidth and spectral overlap constraints is solved by the alternating direction method of multipliers (ADMM), and the precise estimation of the main body is obtained. Experimental results based on both simulated and measured data demonstrate that the proposed algorithm can acquire the high-resolution ISAR image of the main body under noisy and sparse conditions.

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Section: Experimental Results Of Simulated Datamentioning

confidence: 99%