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.
KEYWORDS: Super resolution, Radar imaging, Space based lasers, Reconstruction algorithms, Algorithm development, Detection and tracking algorithms, Radar, Computer simulations, Scattering, Signal to noise ratio
Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation–maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.
KEYWORDS: 3D image processing, Radar, Super resolution, Radar imaging, 3D acquisition, Antennas, 3D vision, Associative arrays, Signal to noise ratio, Stereoscopy
By exploiting the sparsity of radar target image, it is hopeful to obtain a high-resolution target image in multiple-input-multiple-output (MIMO) radar via a sparse representation (SR) method. However, for the three-dimensional (3-D) imaging, the conventional SR method has to convert the 3-D problem into the one-dimensional (1-D) problem. Thus, it will inevitably impose a heavy burden on the storage and computation. A multidimensional smoothed L0 (MD-SL0) algorithm is proposed based on the conventional smoothed L0 algorithm. The proposed MD-SL0 can directly apply to the multidimensional SR problem without transforming to the 1-D case. As a result, a MIMO radar 3-D imaging method via MD-SL0 is achieved with high computation efficiency and low storage burden. Finally, the effectiveness of the method is validated by the results of comparative experiments.
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