Electromagnetic Quantitative Inversion for Translationally Moving Targets via Phase Correlation Registration of Back-Projection Images

A novel electromagnetic quantitative inversion scheme for translationally moving targets via phase correlation registration of back-projection (BP) images is proposed. Based on a time division multiplexing multiple-input multiple-output (TDM-MIMO) radar architecture, the scheme first achieves high-precision relative positioning of the target, then applies relative motion compensation to perform iterative inversion on multi-cycle MIMO measurement data, thereby reconstructing the target's electromagnetic parameters. As a general framework compatible with other mainstream inversion algorithms, we exemplify our approach by incorporating the classical cross-correlated contrast source inversion (CC-CSI) into iterative optimization step of the scheme, resulting in a new algorithm termed RMC-CC-CSI. Numerical and experimental results demonstrate that RMC-CC-CSI offers accelerated convergence, enhanced reconstruction fidelity, and improved noise immunity over conventional CC-CSI for stationary targets despite increased computational cost.

DIFNet: SAR RFI suppression based on domain invariant features

Synthetic aperture radar is a high-resolution two-dimensional imaging radar, however, during the imaging process, SAR is susceptible to intentional and unintentional interference, with radio frequency interference (RFI) being the most common type, leading to a severe degradation in image quality. Although inpainting networks have achieved excellent results, their generalization is unclear, and whether they still work effectively in cross-sensor experiments needs further verification. Through time-frequency analysis of interference signals, we find that interference holds domain invariant features between different sensors. Therefore, this paper reconstructs the loss function and extracts the domain invariant features to improve the generalization. Ultimately, this paper proposes a SAR RFI suppression method based on domain invariant features, and embeds the RFI suppression into SAR imaging process. Compared to traditional notch filtering methods, the proposed approach not only removes interference but also effectively preserves strong scattering targets. Compared to PISNet, our method can extract domain invariant features and holds better generalization ability, and even in the cross-sensor experiment, our method can still achieve excellent results.