针对机载预警雷达空时自适应处理(space-time adaptive processing，STAP)所面临的异构杂波环境，基于杂波和噪声的联合稀疏特性提出了一种直接数据域(direct data domain，D3)STAP方法。首先通过子孔径平滑技术扩充训练样本集合;然后基于杂波谱二阶表征理论构造STAP功率字典矩阵、导出目标函数，并解得待检测单元信号的空时功率谱;最后根据杂波先验信息重构无孔径损失的杂波加噪声协方差矩阵。数值实验验证了所提方法的协方差矩阵估计精度高于传统的稀疏恢复D3-STAP算法，且在理想情况和存在阵列误差的情况下，所提方法皆具备更好的低速目标检测性能。
Direct data domain STAP method via joint-sparse characteristic of clutter and noise
To address the heterogeneous clutter environment of airborne early warning radar in space-time adaptive processing(STAP),a direct data domain(D3)STAP method via joint-sparse characteristics of clutter and noise is proposed.Firstly,training sample set is expanded using the sub-aperture smoothing technique.Then,the STAP power dictionary matrix is constructed based on the second-order characterization theory of the clutter spectrum,and the objective function is derived.Meanwhile,the space-time power spectrum of the signal in the cell under test is solved.Finally,the clutter plus noise covariance matrix without aperture loss is reconstructed according to the clutter prior information.Numerical experiments show that the estimation accuracy of the proposed method is higher than that of the traditional sparse recovery D3-STAP algorithm,and the proposed method has better performance of the low-speed target detection under ideal conditions and array errors.
space-time adaptive processing(STAP)direct data domain(D3)joint sparse characteristicsecond-order characterization of clutter spectrum
万方数据
维普
