Adaptive ADMM-Based High-Quality Fast Imaging Algorithm for Short-Range MMW MIMO-SAR Systems

Short-range millimeter-wave (MMW) multiple-input multiple-output synthetic aperture radar (MIMO-SAR) imaging technology has great development opportunities in the areas of security monitoring, medical diagnosis, and nondestructive evaluation. However, the existing MIMO-SAR fast imaging algorithms typically employ fast Fourier transform (FFT) to invert the target image, which has a serious influence on imaging quality under the highly displayed dynamic range. To address this deficiency, this article presents an adaptive alternating direction method of multipliers (ADMM)-based method on the basis of the state-of-the-art MIMO-SAR fast image-solving model. Different from the traditional ADMM, the proposed algorithm avoids the multiplication and inversion of a large-scale sensing matrix, and the computation is significantly reduced. In addition, adaptive weighting is introduced to adjust the intensity of the
${\ell _{1}}$
regularization item, with the objective of mitigating the loss in imaging accuracy caused by the state-of-the-art fast algorithm. On this foundation, considering the sensitivity of the optimized regularization coefficient to different targets and MIMO array configurations, an efficient optimized regularization coefficient searching method is developed, which greatly enhances the accomplishment of high-quality target image reconstruction in different scenarios as quickly as possible. Numerical simulations and experimental results validate the effectiveness of the proposed algorithm compared with those of the existing FFT-based algorithms.