Optimal design of sparse MIMO arrays for near-field ultrawideband imaging

Near-field ultrawideband imaging is a promising remote sensing technique in various applications such as airport security, surveillance, medical diagnosis, and through-wall imaging. Recently, there has been increasing interest in using sparse multiple-input-multiple-output (MIMO) arrays to reduce hardware complexity and cost. In this paper, based on a Bayesian estimation framework, an optimal design method is presented for two-dimensional MIMO arrays in ultrawideband imaging. The optimality criterion is defined based on the image reconstruction quality obtained with the design, and the optimization is performed over all possible locations of antenna elements using an algorithm called clustered sequential backward selection algorithm. The designs obtained with this approach are compared with that of some commonly used sparse array configurations in terms of image reconstruction quality for various noise levels.