Optimal design of sparse mimo arrays for wideband near-field imaging based on a statistical framework

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2018
Kocamış, Mehmet Burak
Wideband near-field imaging is an emerging 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 achieve high resolution with reduced hardware complexity and cost. In this thesis, based on a statistical framework, an optimal design method is presented for two-dimensional MIMO arrays in wideband near-field imaging. Different optimality criteria are defined based on the image reconstruction quality obtained with the final design. An algorithm called clustered sequential backward selection is used to perform the optimization of the chosen criterion over all initial locations of antenna elements. The developed framework also allows incorporating different practical considerations into the design such as synthetic apertures and antenna patterns. The performance of the approach is illustrated for a microwave imaging application. The designs obtained for different observation settings are compared with some commonly used sparse array configurations in terms of image reconstruction quality. Numerical simulation results suggest that the approach can yield designs that outperform conventional sparse array configurations in terms of image reconstruction quality for a wide range of SNR.