The Undersampling Imaging Method Based on Mmwave MIMO SAR

When using Synthetic Aperture Radar (SAR) for imaging, it is typically necessary to collect a large amount of echo data through a sliding track, which increases the data acquisition time. Clearly, reducing the number of sampling points can directly shorten the time required for the system to collect data. However, when the number of sampling points does not meet the Nyquist criterion, spectral aliasing is introduced, resulting in ghosting artifacts. To address this issue, this paper proposes a compensation method for undersampled data based on smoothing processing. This method utilizes spatial neighborhood information to interpolate and reconstruct the missing data, thereby recovering the lost information. Specifically, the under-sampled data is first compressed in the azimuth direction, and then adjacent data is used to compensate for the missing data, ultimately obtaining full aperture data. The results of image re-construction experiments demonstrate that the proposed method can effectively enhance imaging quality, providing theoretical support and a technical implementation pathway for real-time imaging scenarios in resource-constrained millimeter-wave radar applications.