Enhancement of Forward-Looking Imaging Based on Sparse MIMO Radar via Motion-Based Multiframe Inversion

Radar forward-looking imaging plays a crucial role in achieving high-resolution environmental perception. When a sparse array configuration is employed, the imaging resolution can be improved. However, it is inevitably accompanied by severe grating lobes. Although multi-frame observations can be used to mitigate the influence of grating lobes, they often introduce additional artifacts. Existing approaches remain inadequate in artifact suppression and unavoidably attenuate weak targets. To address these issues, we propose an adaptive weight strategy that leverages the statistical characteristics of multi-frame data to suppress artifacts and improve the fidelity of target amplitude reconstruction. First, the energy of multi-channel signals is computed for each frame, forming a multi-frame energy sequence. Second, an adaptive weight is calculated based on the mean and variance of this sequence. Finally, an exponential factor of the weight is applied to enhance the spectral reconstruction by enlarging the discrimination between true targets and artifacts. The effectiveness of the proposed method is validated through simulation and measurement experiments, demonstrating its advantages over existing methods.