Missing Baseline Recovery Method for Multistatic 3D Imaging Based on Hankel Matrix

The multistatic three-dimensional (3D) synthetic aperture radar (SAR) system integrates the merits of traditional phased array radar's single-pass imaging capability with the flexible baseline of multi-pass tomographic SAR systems, positioning it as a pivotal direction for future advancements. Nonetheless, constrained by cost limitations and safety spacing requirements between platforms, multistatic SAR systems frequently grapple with challenges such as a limited number of baselines and wide baseline spacing, leading to inadequate signal-to-noise ratio (SNR) in data and compromised imaging quality, which in turn hinders the interpretation of 3D results. To tackle this issue, this paper introduces a missing baseline recovery method leveraging Hankel transform and matrix completion. Based on the stack of two-dimensional (2D) imaging results, this method first converts the data vectors into a Hankel matrix, laying the foundation for matrix completion. Subsequently, the low-rank property of the matrix is analyzed. Finally, the missing baseline data is recovered through the matrix completion algorithm. By adopting the above method, the 3D imaging effect is enhanced. The effectiveness of the method is demonstrated through experimental results and analysis.