Strong interference suppression for subspace judgment analysis

When strong interferences are present in underwater environments, the performance of target of interest(TOI) bearing estimation algorithms can be significantly degraded. To address this problem, an interference suppression method for subspace judgment(SSJ) is proposed, which aims to achieve TOI-bearing estimation in the presence of multiple strong interferences. With prior knowledge of the TOI bearing interval, the proposed method builds a judgment item using the correlation between the TOI-interferencenoise subspace and steering vector. The eigenvectors that are not dominated by the TOI are accurately identified via comparison with the estimated judgment threshold. And the identified eigenvectors will be subtracted from the sample covariance matrix(SCM) for interference suppression. The proposed method can obtain a residual SCM that primarily contains the signal of the TOI. It reduces the output power of interference and acquires TOI with a higher signal-to-interference plus noise ratio(SINR), which provides methodical support for improving the capability of TOI-bearing resolution. Simulation and experimental data processing results demonstrate that the proposed method effectively suppresses strong interferences outside TOI-bearing intervals. It reduces the output power of interference and sidelobe levels while improving the capability of TOI-bearing resolution. Compared with other interference suppression methods with subspace, the proposed method has a more robust interference suppression capability.