A Novel Hybrid Domain Algorithm for Planar Near‐Field Millimetre‐Wave SAR Imaging

In the field of near‐field millimetre‐wave synthetic aperture radar (SAR) imaging, the typical range migration algorithm (RMA) and the current hybrid domain algorithm (HDA) are limited by the sidelobe and noise level. In this letter, a novel hybrid domain algorithm is proposed to improve the reconstruction performance. The Fourier transform (FT) and interpolation methods are applied to reconstruct the vertical dimension, and the compressive sensing framework based on the weighted sparse Bayesian learning iterative maximum (WSBRIM) is used to reconstruct the horizontal and range dimensions. The processing methods can effectively suppress the sidelobe and noise level, thereby improving the image quality. Proof‐of‐principle experiments were carried out using a published E‐band planar SAR imaging dataset. The experimental results demonstrated that the proposed algorithm achieves better performance than the RMA and the HDA in terms of visual inspection and evaluation metrics. Our proposed method lays a research foundation for millimetre‐wave non‐destructive testing applications.