A super-resolution method 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 ra...
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A super-resolution method 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. After analysing the 3D echo signal, Kronecker CS (KCS) is introduced to solve the problem of low resolution in 3D image, which is caused by the limited transmit and receivearray. Considering the great 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 accurate recovery. Finally, the effectiveness of the method is validated by the results of comparative simulations.
A super-resolution three-dimensional (3D) imaging method using spectrum estimation theory and narrowband multiple-input-multiple-output (MIMO) radar is presented. First, the 3D echo signal model is built in a narrowba...
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A super-resolution three-dimensional (3D) imaging method using spectrum estimation theory and narrowband multiple-input-multiple-output (MIMO) radar is presented. First, the 3D echo signal model is built in a narrowband bistatic MIMO radar with uniform linear transmit array and uniform rectangular receive array. Then, the high accuracy position estimation of target's scattering centres is obtained via spectrum estimation theory only with single snapshot, and the scatter intensity estimation is obtained by least-square estimation. The 3D image of the target is thus reconstructed. The proposed method is especially suitable for high-speed manoeuvring targets with complex motion. Finally, comparative simulation results are presented and the performances under noise and error case are analysed to demonstrate the effectiveness of the method.
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