Cognitive Antenna Selection in MIMO Imaging Radar

A cognitive antenna selection strategy for multi-in multi-out (MIMO) imaging radar is proposed in this article. The basic idea of our strategy relies on the dynamic selection of the antenna location according to the feedback information to enhance the image quality and reduce the computational burden simultaneously. The aim of our strategy is to indirectly minimize the mean squared error associated with the amplitudes and positions of the strong scattering centers of the target through the frame potential. Specifically, it is assumed that the imaging process is initially performed via a conventional uniform linear array/random sparse array of collocated MIMO radar; hence, based on the collected data, an initial image of the target is derived (perception). Then, the accuracy of low-resolution images is enhanced progressively according to the cognitive paradigm via a specific antenna location selection at the next transmission (action). Benefit from the enhanced accuracy, the support area of targets can be estimated to reduce the dimension of the undersampling matrix and, finally, the computational burden in the subsequent high-resolution image reconstruction process is reduced. The simulation results highlight the capabilities of our cognitive approach to provide more interesting benefits in imaging than the random selection strategy and demonstrate the enhanced imaging performance of cognitive sparse MIMO array under the condition of limited antennas and noise.