Sparsity-based three-dimensional image reconstruction for near-field MIMO radarimaging

Abstract

Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, airport security, concealed weapon detection, and surveillance. The successful operation of these radar imaging systems highly depends on the quality of the images reconstructed from radar data.Since the underlying scenes can be typically represented sparsely in some transform domain, sparsity priors can effectively regularize the image formation problem and hence enable high-quality reconstructions. In this paper, we develop an efficient three-dimensional image reconstruction method that exploits sparsity in near-field MIMO radar imaging.Sparsity is enforced using total variation regularization, and the reflectivity distribution is reconstructed iteratively without requiring computation with huge matrices. The performance of the developed algorithm is illustrated through numerical simulations. The results demonstrate the effectiveness of the sparsity-based method compared to a classical image reconstruction method in terms of image quality.

Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

“…Different than direct inversion methods, regularized iterative reconstruction methods incorporate additional prior information (such as sparsity) into the reconstruction process to eliminate uniqueness and noise amplification issues arising due to limited data and measurement noise. With the advent of compressed sensing (CS) theory [23], sparsitybased reconstruction is the most commonly used analytical regularization approach and has been widely studied in various imaging problems [59,60], including radar imaging both for far-field and monostatic imaging settings [24][25][26][27][28][29][30], as well as for multistatic and near-field settings [31][32][33][34].…”

“…These algorithms are generally adapted from sparsity-based reconstruction algorithms developed for two-dimensional image restoration problems and mainly differ from each other in their efficiency in terms of computation time and memory usage. For example, Cheng et al [33] adapted the split augmented lagrangian shrinkage algorithm (SALSA) [60] and Oktem [32] adapted the half-quadratic regularization approach [62]. There have been also some efforts to reduce the computational cost and memory usage of such sparsity-based reconstruction algorithms by exploiting the special structure of the forward problem [8,34].…”

“…Regularized reconstruction methods on the other hand incorporate additional prior information (such as sparsity) into the reconstruction process. With the advent of compressed sensing (CS) theory [23], sparsity-based reconstruction methods are the most commonly used model-based iterative inversion methods in various imaging problems including radar imaging, both for far-field and monostatic imaging settings [8,[24][25][26][27][28][29][30], as well as for multistatic and near-field settings [31][32][33][34]. Although sparsity-based methods provide better reconstruction quality than the traditional direct inversion methods at compressive settings, they suffer from high computational cost which is undesirable in real-time applications.…”

“…Although sparsity-based methods provide better reconstruction quality than the traditional direct inversion methods at compressive settings, they suffer from high computational cost which is undesirable in real-time applications. Moreover, regularized methods in the near-field radar imaging literature generally enforce smoothness or sparsity on the complex-valued reflectivity distribution [8,32]. These methods are therefore built on the assumption that the phase and magnitude of the scene reflectivity exhibit local correlations.…”

“…Each transmit antenna with location (x t , y t , 0) illuminates the scene that is in the near-field of the array. Using Born approximation for the scattered field, we can express the time-domain signal acquired by the receive antenna located at (x r , y r , 0) due to a single scatterer at (x, y, z) with reflectivity s(x, y, z) as follows [20,32]:…”