A subspace spatial smoothing-based sparse reconstruction passive direction-of-arrival estimation method under strong interference

Passive direction-of-arrival (DOA) estimation of weak targets under strong interference is usually challenging, due to the lack of prior information about the targets. When strong interferences and weak targets are closely spaced and the interference signals are strongly correlated or even coherent with the target signals, the DOA estimation of weak targets can become even more difficult. To address this problem, a subspace spatial smoothing-based sparse reconstruction passive DOA estimation method is proposed. In this method, the sample covariance matrix is projected into the signal subspace to mitigate the adverse effect of interference on the target signal. Subsequently, the modified enhanced spatial smoothing technique is applied to the signal subspace, which not only enhances robustness to correlated signals but also improves the accuracy of covariance reconstruction. Furthermore, a grid evolution method is developed to improve the utilization efficiency of grid points, significantly reducing the computational complexity while remaining a reasonable DOA estimation accuracy. Simulations and experimental results demonstrate that, when strong interferences and weak targets are closely spaced, the proposed method achieves higher resolution and DOA estimation accuracy compared to existing DOA estimation methods. Additionally, it exhibits high computational efficiency and robustness to coherent signals.