基于数据平滑的降维STAP混响抑制方法

The reduced rank STAP for reverberation suppression based on data smoothing

分析了主动声呐采用空时自适应处理进行混响抑制的困难,给出了一种有效的降维空时自适应处理方法。该方法把待检测的空时数据划分为子组进行空时自适应处理,采用空时前后向平滑的方法来增加有效的样本数据数量,提高协方差矩阵的估计精度。通过分析子组输出间的相位关系,指出可以对所有子组的输出进行相干叠加。不仅能进一步提高输出信混比,还能减小空间和时间孔径的损失。通过仿真的混响数据比较了不同方法的改善因子,表明该方法能够有效消除强混响,而且具有较高的稳健性。

The difficulty of space time adaptive processing（STAP）in active sonar reverberation suppression is analyzed, and a valid reduced-rank STAP method is given. The method carries out STAP after dividing the data to be detected into subgroups, and the number of valid samples increases greatly by using the techniques of forward/backward spatial smoothing and temporal smoothing which will improve the estimating precision of the covariance matrix. By analyzing the phase relationship between different subgroups＇ outputs, it＇s pointed out that the outputs of all subgroups can be summed coherently. The output signal to reverberation ratio can be improved, and the loss in space-time aperture can be decreased. The improvement factors of different methods are compared by using the simulated reverberation data, and it is shown that the method can eliminate the strong reverberation efficiently and has a higher robustness.