基于联合时间维训练样本的非平稳杂波抑制方法

针对机载雷达的杂波距离依赖性导致空时自适应处理(STAP)器性能下降的问题,提出一种基于回波快-慢时间二维训练样本的非平稳杂波抑制方法。利用脉冲雷达回波信号的时域平稳性以及杂波多普勒频率随距离缓变的特点,在STAP 时域分段的降维处理中,利用慢时间和快时间维的数据样本联合估计协方差矩阵,达到减小距离向杂波非平稳的目的;对所有滤波器输出进行相干叠加,减小降维引起的孔径损失,提高输出信杂噪比。将该方法应用于非正侧视阵机载雷达杂波抑制中,仿真结果表明不仅能显著提高协方差矩阵的估计精度和主瓣杂波抑制性能,而且具有较高的稳健性。

Nonstationary Clutter Suppression Based on Joint-time Secondary Data Selection

The range dependence is one of the intrinsic features for the clutter of airborne radar, which degrades the performance of conventional space-time adaptive processor (STAP). A novel algorithm of nonstationary clutter suppression, which is based on the joint-time secondary data selection, is presented. Due to the stationarity of radar echo in time domain and the slow change in the Doppler frequency with range, both the slow and quick time secondary data are used as the training samples for sub-CPI adaptive processing to mitigate the range dependence, and ultimately the loss of time aperture is decreased by summing the outputs of all the sub-processors coherently. The simulation results indicate that the algorithm can improve the precision of covariance matrix and the performance of main-lobe clutter suppression, and has a higher robustness.