A Robust Sparse Imaging Algorithm Using Joint MIMO Array Manifold and Array Channel Outliers

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 16; p. 4120
• Main Authors: Ding, Jieru, Wang, Zhiyi, Wu, Xinghui, Wang, Min
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Algorithms; alternative direction method of multipliers (ADMM); Bandwidths; Convergence; Image reconstruction; Inverse synthetic aperture radar; low-rank matrix completion; lp-norm; Maneuvering targets; Manifolds; MIMO communication; Motion compensation; multiple-input multiple-output (MIMO) radar; Optimization; Outliers (statistics); Radar; Radar imaging; Recovery; Remote sensing; Schatten p-norm; shrinkage; Signal processing; Sparsity; synthetic aperture radar
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14164120

The multiple-input multiple-output (MIMO) radar imaging technology has attracted many scholars due to its many inherent advantages, such as avoiding complex motion compensation and imaging a quickly maneuvering target, compared to inverse synthetic aperture radar (ISAR) imaging. Although some imaging algorithms, such as the 2D fast iterative shrinkage thresholding algorithm (2D-FISTA), can meet the demand for super-resolution, they are not directly suited to MIMO radar imaging, for which the MIMO manifold needs to be considered. In this paper, based on the above questions, we propose the MIMO radar imaging algorithm, utilizing the sparsity of the scattering map in space and the MIMO array manifold, even achieving a good performance in the presence of MIMO channel error. The sparse reconstruction algorithm is developed with the alternative direction method of multipliers (ADMM) with the help of 2D-FISTA and the lp-norm. Then, two algorithms are derived: one is the exact sparse recovery algorithm, and the other is the inexact sparse recovery algorithm. Although the exact sparse recovery algorithm can converge to a more accurate precision than the inexact algorithm, the latter can converge at a faster speed. Finally, the results on simulation data validated the effectiveness of the algorithm.