非正侧阵机载雷达杂波谱迭代自适应配准方法

对于机载非正侧阵雷达,杂波谱的距离空变性使得直接利用邻近单元数据估计的协方差矩阵与真实协方差矩阵不匹配,导致空时自适应杂波抑制性能下降。基于配准的补偿(registration based compensation, RBC)方法通过时域平滑来估计空时谱,继而对地杂波的距离依赖性进行补偿,由于时域孔径损失使杂波谱峰值的估计精度受到限制。针对这一问题,考虑到杂波空时谱的稀疏性,本文利用迭代自适应算法(iterative adaptive approach, IAA)进行空时谱估计,并重构各辅助距离单元协方差矩阵以实现杂波谱配准,称为IAA RBC算法。由于该算法不存在孔径损失,杂波谱配准精度更高。仿真实验表明,即使在样本中存在目标污染等干扰因素的情况下,所提方法相比传统的利用时域平滑的RBC方法也能获得更好的信噪比改善。

Registration based compensation using iterative adaptive approach in non-side looking airborne radar

The range dependent nature of the clutter power spectrum in non-side looking airborne radar systems results in a mismatch of the clutter covariance matrix computed from the secondary data relative to the true one, and poor performance of traditional space time adaptive processing (STAP) algorithms. Registration based compensation (RBC) is implemented based on a sub snapshot spectrum using temporal smoothing, the estimation accuracy of the clutter spectrum is limited due to temporal aperture loss. In view of this problem, the technique of iterative adaptive approach (IAA) for estimation of the spatial temporal spectrum is introduced according to the sparsity, then the covariance matrix for the secondary cell is constructed and brought into registration, namely RBC with IAA (IAA RBC). In this case, the clutter spectrum is obtained with higher accuracy because that aperture loss is not exist. Numerical examples shows that, in terms of signal to inference plus noise ratio improvement, better performance is obtained with the developed method compared to the traditional RBC method of using temporal smoothing, even in the presence of other interferers, such as targets.