Interrupted Sampling Repeater Jamming Suppression for Wideband Radar With MCA-ADMM-Net.

Abstract

Wideband radar is widely used for imaging and recognition in both military and civilian applications, such as remote sensing and guided strikes. With the advancement of electronic support systems, wideband radar is increasingly susceptible to complex electromagnetic environments. Among these jamming types, interrupted sampling repeater jamming (ISRJ) exhibits characteristics such as rapid interrupted sampling, high power, strong correlation, and complex modulation, which can generate numerous false targets and lead to misjudgments in radar systems. To address this issue, we propose an ISRJ suppression method based on joint morphological component analysis (MCA) and deep unfolding networks. We analyze the structural differences between target echoes and ISRJ signals, which can be sparsely represented under two approximately orthogonal dictionaries. The MCA optimization model is then introduced to simultaneously estimate the target echoes and ISRJ. To circumvent the challenges of hyperparameter determination, we derive an unfolding network based on the alternating direction method of multipliers (ADMM), enabling adaptive hyperparameter selection and optimization solutions with limited iterations in a data-driven manner. This method offers the advantages of adaptive hyperparameter tuning and robustness to varying jamming-to-signal-and-noise ratios (JSNRs). In addition, it achieves simultaneous jamming suppression and complete target echo preservation within the MCA framework. Finally, simulation and real-world experiments validate the effectiveness of this approach.