Enhancing mmWave Automotive Forward-Looking MIMO-SAR Imaging by Space–Time Sparsity

Millimeter-wave (mmWave) automotive multiple-input–multiple-output forward-looking synthetic aperture radar technology (FL-MIMO-SAR) can achieve high-resolution images for advanced driver assistance systems. However, the conventional FL-MIMO-SAR imaging methods suffer from high sidelobe, noise interference, and relatively low imaging resolution in the FL region. To tackle these challenges, we propose a space–time sparsity-based (STSP) imaging method to enhance automotive FL-MIMO-SAR images. First, different from conventional FL-MIMO-SAR imaging models, we develop an STSP imaging model. Specifically, we combine the azimuth and Doppler steering matrices to construct the measurement matrix, which can retain the ability to resolve left and right ambiguities while improving the azimuth resolution. Meanwhile, we employ the sparsity prior of targets in the space and time dimensions as an STSP constraint, which not only suppresses high sidelobes and noise but also further enhances imaging resolution, especially in the FL region. Then, to enable efficient STSP imaging along range dimension, the echo data are detected to reduce the dimension of the effective data and the computational complexity. Meanwhile, we also derived an efficient linear approximation method to solve this model. Finally, simulations and measured automotive radar experiments confirm that our proposed method can effectively enhance imaging quality while maintaining efficient computing performance, especially in the case of echo data undersampling.