Suppression of Mainlobe Interference in Radar Network via Joint Low-rank and Sparse Recovery

The challenge of effectively suppressing interference in radar systems, particularly the complex and unknown mainlobe interference, is a significant concern in radar signal processing. Traditional anti-jamming methods in single radar often fail to address this issue. The paper proposes a novel approach for suppressing mainlobe interference in radar networks, capitalizing on the low-rank representation of interferences and the sparse representation of echoes. Interference signals can be extracted by minimizing their rank with a regularization constraint after performing range and Doppler equalization on the received signals. Target echoes can be recovered through joint sparse reconstruction, exploiting their unique motion states across multiple observation points. To solve the underlying optimization problem, which involves the simultaneous reconstruction of low-rank and sparse matrices, we propose two algorithms based on the augmented Lagrangian method (ALM), with one algorithm focusing on precision and another emphasizing efficiency. This method leverages the robust spatial correlation of the interference signal and the sparsity of the target spatial distribution, allowing for effective interference suppression and accurate target echo recovery without prior knowledge of the interference type. Numerical experiments validate the effectiveness of this proposed approach and its superiority compared with other methods.