Norm-1 Regularized Consensus-Based ADMM for Imaging With a Compressive Antenna

This letter presents a novel norm-1-regularized, consensus-based imaging algorithm, based on the alternating direction method of multipliers (ADMM). This algorithm is capable of imaging metallic targets by using a limited amount of data. The distributed capabilities of the algorithm enable a fast imaging convergence. Recently, a compressive reflector antenna (CRA) has been proposed as a way to provide high sensing capacity with a minimum cost and complexity in the hardware architecture. The ADMM algorithm applied to the imaging capabilities of the CRA outperforms current state-of-the-art iterative reconstruction algorithms, such as Nesterov-based methods, in terms of computational cost, enabling the use of the CRA in quasi-real-time, compressive sensing imaging applications.

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