Multiple-input–multiple-output radar super-resolution three-dimensional imaging based on a dimension-reduction compressive sensing


A super-resolution methodology for three-dimensional (3D) imaging by combining a narrowband multiple-input-multiple-output (MIMO) radar and compressive sensing (CS) theory is presented. First, a narrowband bistatic MIMO radar with uniform linear transmit array and uniform rectangular receive array is proposed. Once analysing the 3D echo signal, Kronecker CS (KCS) is introduced to unravel the matter of low resolution in 3D image, that is caused by the limited transmit and receive array. Considering the nice complexity of KCS in improving the 3D resolution jointly, a dimension-reduction CS approach is presented to reduce its storage and computation burden. Furthermore, the restricted property of the dimension-reduction dictionary is analysed to insure the correct recovery. Finally, the effectiveness of the method is validated by the results of comparative simulations.

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