3D IMAGE RECONSTRUCTION ALGORITHM FOR A SPARSE ARRAY RADAR SYSTEM BASED ON COMPRESSIVE SENSING

We developed a SFCW sparse array radar system for 3D imaging, which consists from 16 transmitters and 16 receivers and operates at the frequency range from 270MHz to 8GHz. The 2D antenna array layout was optimized using the middle point approximation. Several image reconstruction methods, which include the least square, Compressive Sampling Matching Pursuit and Regularized Orthogonal Matching Pursuit were developed and applied to the data acquired by the system to improve the resolution and suppress the artifacts caused by the antenna sparsity. The developed methods showed better performance compared to the conventional ones while they have the equivalent robustness. In addition, we propose to include the antenna radiation pattern into the image reconstruction algorithm, and we demonstrated that it gives better convergence and reduces artifacts on the results for the near range measurements. The proposed methods was successfully validated by the laboratory experiments.