Sparse tensor recovery based method for MIMO radar high resolution three-dimensional imaging

To improve the imaging quality and reduce the computation burden, this paper proposes a sparse tensor recovery based method for multiple-input multiple-output (MIMO) radar 3D imaging. Firstly, by constructing the sensing matrices in the range direction and angle directions in a pseudo polar coordinate, the sparse tensor recovery model for target 3D imaging is established. Then, the tensor sequential order one negative exponential (Tensor-SOONE) function is proposed to measure the sparsity of the received signal tensor. At last, the gradient projection (GP) method is employed to effectively solve the sparse tensor recovery problem to get the 3D image of targets. Compared to conventional imaging methods, the proposed method can achieve a high-resolution 3D image of targets with reduced sampling number. Compared to existing sparse recovery based imaging methods, the proposed method has a higher accuracy and robustness, while the computational complexity is relatively small. Simulations verify the effectiveness of the proposed method.