基于特征子空间的强主瓣干扰抑制方法

Strong Mainlobe Interference Suppression Method Based on Eigen-Subspace

  • 摘要: 当空间中存在大功率主瓣干扰时,传统特征投影类干扰抑制方法存在主瓣峰值偏移、旁瓣内干扰零陷变浅等问题。针对上述问题,本文提出一种新的基于特征子空间的主瓣干扰抑制方法。首先根据赤池信息准则(AIC)与相关性判别分离主、旁瓣干扰的特征矢量;其次利用特征投影预处理抑制主瓣干扰分量;最后定义新的权矢量赋形方式,在主瓣子空间约束及旁瓣干扰线性约束下进行自适应波束形成。仿真实验结果表明,所提方法达到了更为稳健的方向图保型效果,获得了趋近于理想零值的干扰处零陷增益,提高了1.3dB以上的输出信干噪比(SINR)。

     

    Abstract: When the mainlobe interference with high power exists in the space, the traditional interference suppression methods based on eigen-projection have some problems, such as mainlobe peak shift, shallow sidelobe notches and so on. In view of the above problems, a new method for suppressing mainlobe interference based on eigen-subspace is proposed in this paper. Firstly, the eigenvectors of mainlobe and sidelobe interferences are determined according to Akaike Information Criterion (AIC) and correlation discrimination. Secondly, the mainlobe interference component is suppressed by eigen-projection preprocessing. Finally, by defining a new configuration method of weight vectors, adaptive beamforming is performed under the constraint of mainlobe subspace and the linear constraints of sidelobe interferences. Simulation results show that the proposed method achieves a more robust pattern preserving effect, obtains a notch gain close to the ideal zero value of each interference, and improves the output Signal to Interference plus Noise Ratio (SINR) by more than 1.3dB.

     

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