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

Tao Pu; Ningning Tong; Weike Feng; Bin Xue; Pengcheng Wan

doi:10.1117/12.2589502

20 January 2021 Sparse tensor recovery based method for MIMO radar high resolution three-dimensional imaging

Tao Pu, Ningning Tong, Weike Feng, Bin Xue, Pengcheng Wan

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 117190F (2021) https://doi.org/10.1117/12.2589502
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

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.

Citation Download Citation

Tao Pu, Ningning Tong, Weike Feng, Bin Xue, and Pengcheng Wan "Sparse tensor recovery based method for MIMO radar high resolution three-dimensional imaging", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 117190F (20 January 2021); https://doi.org/10.1117/12.2589502

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