Strong interference suppression for subspace judgment analysis

In the presence of strong underwater interferences, the performance of existing target of interest (TOI) bearing estimation algorithms significantly degrades. In this paper, a subspace judgment (SSJ)-based interference suppression method is proposed, which aims to enhance the ability of TOI-bearing estimation under multiple strong interferences. Specifically, with prior knowledge of the TOI bearing interval, the proposed method builds a judgment item exploiting the correlation between the TOI-interference-noise subspace and steering vector. Then the eigenvectors not dominated by the TOI can be accurately identified with a comparison of the aforementioned threshold. Finally, the identified ones will be subtracted from the sample covariance matrix (SCM). As a result, a residual SCM that primarily contains TOI is obtained, which provides methodical support for improving the capability of TOI-bearing resolution. Simulation and experimental results demonstrate that the method effectively suppresses strong interferences outside TOI-bearing intervals. It also reduces the output power of interference and sidelobe levels while improving the capability of TOI-bearing resolution. The proposed method outperforms other state-of-the-art subspace-based interference suppression methods.